Conventional feeding of lactating sows in commercial farrowing units was often constrained by coarse feed control, fixed meal timing, and feed losses caused by rooting and manipulation at the trough. These limitations frequently resulted in unstable feed intake during mid- to late lactation, which in turn increased the likelihood of excessive body-reserve mobilization and delayed reproductive recovery after weaning. To address these problems, this study adopted a two-stage progressive experimental design to systematically evaluate the effects of an intelligent precision feeding system on sow feed intake, body condition, reproductive efficiency, and piglet growth performance, and to identify key operating parameters. The study was conducted in a commercial farrowing facility in Shaanxi, China, from March to June 2025. The intelligent feeding unit integrated controlled-dose dispensing with event-based data logging and a trigger mechanism that delivered small top-up portions when trough residual feed and short-window interaction signals indicated persistent feeding motivation; a water–feed mixing option was implemented through a water-control module. Daily feed intake and daily water consumption were recorded automatically. Sow body condition was assessed using backfat thickness measured at entry to the farrowing crate and at weaning. Reproductive recovery and piglet performance traits were obtained from the farm production-record system. In Experiment I, ninety-four multiparous sows were allocated to an Intelligent Feeding Group (IFG) or a Traditional Feeding Group (TFG) (47 sows per unit) housed in environmentally comparable units. IFG sows were managed using the hybrid logic combining a stage-wise baseline allowance with sow-initiated triggered top-ups, whereas TFG sows were fed with a conventional dry-feed feeder at fixed times (three meals per day). The intelligent system produced more stable intake trajectories, and the clearest separation between treatments emerged during mid- to late lactation. From approximately day 15 postpartum onward, daily feed intake in IFG exceeded that of TFG by about 5%-10%, reflecting a more sustained intake plateau and a later decline toward the end of lactation. Consistent with improved intake stability, body-reserve mobilization was reduced in IFG as indicated by backfat change: mean backfat loss decreased from 2.61 mm in TFG to 1.69 mm in IFG (P<0.01), corresponding to a 35.2% reduction. Reproductive recovery was accelerated in IFG. The weaning-to-estrus interval (WEI) shortened from 9.04 days (TFG) to 8.15 days (IFG) (P<0.01), and the wean-to-service rate increased from 85% to 90%. Piglet outcomes also improved in association with the stabilized maternal intake pattern: average weaning weight increased from 7.30 kg (TFG) to 7.68 kg (IFG) (P<0.01), pre-weaning weight gain rose from 6.06 to 6.47 kg (P<0.05), and pre-weaning survival increased from 89.13% to 92.06%. Together, these results indicated that the intelligent feeding approach supported higher and more persistent feed intake during late lactation and aligned with improved body-condition preservation and superior reproductive and litter performance. In Experiment II, parameter screening was performed within two units equipped with the intelligent feeding system to compare two deployable “meal frequency × dilution” strategies under identical hybrid logic. A five-meal strategy with a water-to-feed ratio of 1.3:1 during mid- to late lactation was compared with a four-meal strategy with a water-to-feed ratio of 1.5:1. The five-meal strategy with a water-to-feed ratio of 1.3:1 maintained a higher and more persistent intake plateau between days 12 and 21 postpartum and delayed the late-lactation decline compared with the four-meal strategy with a water-to-feed ratio of 1.5:1. Daily water consumption showed a similar temporal pattern, with a sharper late-lactation decline under the four-meal strategy with a water-to-feed ratio of 1.5:1. Backfat-change outcomes during parameter screening were consistent with these temporal intake differences. The five-meal strategy with a water-to-feed ratio of 1.3:1 showed a more favorable backfat-loss profile, with smaller loss and a more concentrated distribution with fewer extreme negative values. Because meal frequency and water-to-feed ratio were coupled in this screening comparison, mechanistic interpretation was limited to the combined strategy rather than isolated main effects. In conclusion, a sensor-based, trigger-activated phased precision feeding approach provides a practical and traceable framework for stabilizing feed and water intake during mid- to late lactation in commercial farrowing systems. This approach reduces body-reserve mobilization, as indicated by lower backfat loss, supports faster post-weaning reproductive recovery, and improves piglet growth and survival under the tested conditions. Within the evaluated operating settings, the five-meal strategy with a water-to-feed ratio of 1.3:1 represents a promising deployable configuration for sustaining the late-lactation intake plateau and mitigating the end-of-lactation decline in intensive swine production.

To address the feed arching phenomenon that occurs during the feeding process of beef cattle and to satisfy the individualized feeding requirements of beef cattle, a roller-brush type supplementary feeding and pushing robot was designed in this study, which consists of a roller-brush pushing device, a screw-type supplementary feeding device, and an Ackermann chassis. The structural design of the feeding screw and the pushing roller brush was completed, and the motion behavior of feed particles was analyzed. In order to investigate the influence of the motion parameters of the supplementary feeding and pushing robot on the feeding and pushing performance, a simulation analysis of the robot’s motion process was carried out based on the EDEM-RecurDyn coupling method. First, the contact parameters among total mixed ration (TMR) particles as well as between the feed and the mechanical components were determined. Subsequently, dynamic models of the supplementary feeding device and the pushing device were respectively constructed in RecurDyn, and a flexible mesh was generated for the roller brush. Finally, a feed particle model was built in the EDEM software, and the device models were imported to complete the coupled simulation. In the study of feeding performance, the screw rotation speed and the robot’s travelling speed were taken as experimental factors, while feeding uniformity and feeding efficiency were used as evaluation indicators. In the study of pushing performance, the roller brush rotation speed, the roller brush deflection angle, and the robot’s travelling speed were taken as experimental factors, and the pushing rate and pushing efficiency were used as evaluation indicators. Single-factor and orthogonal experimental methods were adopted for the simulation tests. The simulation results showed that when the screw rotation speed of the supplementary feeding and pushing robot was 160 r/min, the robot travelling speed was 0.68 m/s, the roller brush rotation speed was 450 r/min, and the roller brush deflection angle was 40°, the feeding uniformity exceeded 96%, the feeding efficiency reached 120.6 kg/min, the pushing rate was 98.25%, and the pushing efficiency was 418.94 kg/min. Prototype tests were carried out under these optimal parameters, and the obtained results were as follows: feeding uniformity greater than 93%, feeding efficiency of 135.8 kg/min, pushing rate of 97.90%, and pushing efficiency of 311.90 kg/min. The designed robot exhibits good working performance and can meet the auxiliary feeding requirements of small- and medium-scale cattle barns.

Immunization injection is of ten require d to prevent and control the diseases in pig farming. However, current needle injection still relies on manual labor in pig farms, leading to serious challenges, such as high risk of needle breakage, severe cross-infection, low efficiency, time-consuming, and labor-intensive operation, as well as missed or incorrect injections. In this study, a control system was proposed for vision-based positioning and motion in a needle-free injection robot under confined stall breeding scenarios. According to the workflow of swine immunization, the key components were selected to integrate the navigation system of the robot. Its parameters were then determined to improve the needle-free injection module. The motion range of the robotic arm was simulated using MATLAB. The rotation angles of its six axes were constrained to prevent collision between the robotic arm and confined stall. A precise localization was proposed for the needle-free injection site on pigs. The YOLOv8n framework was improved to solve the missed and false detections caused by blurred features of the tail root and susceptibility to dirt interference. The improved network was used to extract the tail root region from pig images. A point set of the hip muscle injection area was constructed for vertical injection. The least squares method (LSM) was applied to fit the surface point set, enabling accurate calculation of the injection point and posture. A control system was developed to accurately track the injection point in the needle-free injection robot, due to the random movement of pigs during operation. According to feeding system, the injection was divided into two phases: a pre-injection and an injection phase. In the pre-injection phase, the tail root position was continuously detected to perform visual servoing control of robotic arm, thereby tracking the injection point in real time. In the injection phase, the robot moved into the injection point, and then performed the injection, according to the last detected pose. The critical distance of phase transition was determined to be 15 cm using depth camera and triangulation. The robot operating system (ROS) was used to integrate navigation, visual detection, and robotic arm control algorithms, enabling the robot to follow a predetermined trajectory and then perform needle-free injection on pigs. Experiments were conducted at a pig farm in Qinhuangdao City, Hebei Province, China. The experimental results showed that the tail root detection algorithm achieved an accuracy of 95.8%, a recall of 93.5%, and a mean average precision (mAP) of 97.1%. The overall injection success rate of the robot was 93.3%, of which 95.2% were vertical injections. The maximum injection deviations in the X, Y, and Z axes were 2.9, 3.7, and 1.9 cm, respectively, corresponding to average deviations of 1.30, 1.91, and 0.59 cm. The injection accuracy was fully met the requirements of swine immunization. A closed-loop control system was realized from chassis navigation, visual recognition, dynamic tracking to precise triggering for the needle-free injection in real scenarios. The findings can also offer the technical and engineering reference for the large-scale intelligent farming.

Holistic sidewall ventilation system (HSVS) is characterized by uniform temperature distribution in the poultry house. Yet two challenges remain: low air velocity at the front cross-section and suboptimal positions of recirculation zones in large-scale facilities. The airflow field is governed by the configuration and regulation of air inlets. In this study, the airflow distribution was optimized to enhance overall ventilation performance in HSVS poultry houses. The opening angles of local front inlets were also adjusted using field measurements and numerical simulations. A systematic analysis was implemented to explore the effects of inlet angles on the airflow field. A full-scale HSVS poultry house was selected as the research object, where 118 sidewall air inlets were divided into front, middle, and rear segments (40, 40, and 38 inlets, respectively). Two ventilation scenarios (with 3 and 4 operational fans) were evaluated, wherein the opening angles of the front inlets were adjusted within the range of 10° to 90°, whereas the middle and rear air inlets were kept at fixed angles to match the ventilation scenarios. Air velocity and pressure difference were continuously measured at 6 sensor points in the laying hen activity zone (1.5 m above the ground). Computational fluid dynamics (CFD) incorporated with the Reynolds-averaged Navier-Stokes (RNG) k-ε turbulence model was adopted to simulate the airflow field. The air velocity non-uniformity coefficient was employed as the evaluation metric to quantify the uniformity of airflow distribution. The results showed that different ventilation scenarios displayed an identical variation trend. The opening angle of the front air inlets reduced the static pressure difference between the inlets and the outdoors, while preserving a uniform distribution of pressure difference. The average pressure difference in the middle section of the poultry house was higher than that at the front end, with an average difference of (1.7±0.2) Pa. This discrepancy was also independent of both the inlet opening angle and the ventilation scenario. Meanwhile, air velocity increased with an increase in the opening angle of the front air inlets, indicating a negative correlation with pressure difference. Under the scenario with 3 operational fans, the maximum average air velocity in the front and middle segments reached (0.18±0.02) m/s (at 70°) and (0.30±0.06) m/s (at 90°), respectively; Under the scenario with 4 operational fans, these values were (0.25±0.04) m/s (at 90°) and (0.38±0.06) m/s (at 90°), respectively. Furthermore, the front inlet opening angle was adjusted to alleviate inadequate ventilation in the front section of the house, indicating a moderate improvement in air velocity in the middle section. The air velocity non-uniformity coefficient decreased consistently, as the inlet angle increased, thus dropping to below 0.30 at the angle of 70° or larger (0.24 for 3 fans and 0.30 for 4 fans at 70°). Subsequently, two optimal operating conditions were selected for further CFD simulation analysis. CFD simulation results showed that the front inlet angles enhanced the overall indoor air velocity, where the average air velocity at the front cross-section increased by 0.10 m/s. The optimal inlet angles effectively mitigated front ventilation dead zones for the airflow uniformity, indicating less unfavorable recirculation zones. The opening angle of the front air inlets was adjusted to 70° or larger for the weak ventilation zone at the front of HSVS poultry houses, indicating indoor airflow uniformity. Zoning regulation of air inlet angles can offer a cost-effective and efficient solution to enhance the ventilation performance of HSVS poultry houses. The finding can provide the theoretical basis and technical support for environmental control optimization in large-scale poultry houses. Future research can be expected to integrate the heat and mass exchange between hens and the environment in smart agriculture.

Thermal and humidity environments can dominate the pig growth, health status, and production performance in pig houses, including air temperature, relative humidity, and airflow velocity. The environment can be regulated to consider the interaction mechanism between housing conditions and pig thermal responses. A mechanistic and physiologically interpretable model is required to accurately simulate pig thermal responses under different thermal and humidity conditions. However, existing models of pig thermal response cannot fully meet the requirements of the intelligent control applications. In this study, a pig two-node heat exchange model (PTHM) was established using biological heat balance theory and thermodynamics. Heat exchange was also simulated among the core, the skin layer, and the surrounding environment. Metabolic heat was generated in the core layer and then transferred to the skin via tissue conduction and blood circulation. Part of the heat was dissipated to the environment as sensible respiratory heat loss. The remaining heat was stored within the body, leading to an increase in rectal temperature. Heat in the skin layer was transferred from the core via conductive transfer and blood-mediated convective transport. The heat was then dissipated to the surrounding environment via convective heat exchange and thermal radiation. A small fraction of heat was dissipated after skin evaporation. Environmental parameters were used as the model inputs, while the major physiological parameters were used as the outputs after simulations. A recognition framework of pig drinking behavior was developed using an improved YOLOv11 object detection architecture, particularly for the prediction accuracy and physiological interpretability of the model. A pig drinking detection model (PDDM) was further established to calculate drinking frequency using this framework. The drinking frequency was then introduced into the PTHM as a behavioral correction factor to regulate blood-mediated convective heat transfer and respiratory heat dissipation, thereby constructing a drinking behavior–corrected pig two-node heat exchange model (D-PTHM). A more realistic representation was obtained for the pig thermoregulation. The results showed that the air temperature was the dominant environmental factor on pig thermal physiological responses. The PTHM model also achieved coefficients of determination (R²) of 0.673, 0.685, and 0.615 for rectal temperature, heart rate, and respiratory rate, respectively. The mean absolute errors (MAE) were 0.320 °C, 7.020 bpm, and 0.916 bpm, while the root mean square errors (RMSE) were 0.412 °C, 9.120 bpm, and 1.635 bpm, respectively. A preliminary representation was obtained for the heat transfer pathway from the body core to the skin. Subsequently, the surrounding environment was offered a simplified representation of whole-body heat balance. The DCB-YOLO drinking detection model achieved a mean average precision (mAP) of 97.47%. The PDDM was used to reliably quantify the pig drinking frequency for behavioral correction of the heat exchange model. The prediction accuracy of D-PTHM was significantly improved after drinking behavior was introduced as a correction factor. The D-PTHM achieved higher R² values of 0.831, 0.771, and 0.775 for the rectal temperature, heart rate, and respiratory rate, respectively. The MAEs were 0.247 °C, 3.358 bpm, and 0.580 bpm, while the RMSEs were 0.332 °C, 4.053 bpm, and 0.747 bpm, indicating the improved model stability and environmental adaptability. The drinking behavior significantly enhanced the mechanistic model to regulate the pig thermal field under different thermal and humidity conditions. This finding can provide a physiologically realistic model for precision environmental control in pig houses. More accurate environmental regulation can be used to improve animal welfare using pig physiological responses in sustainable and efficient livestock production.

Multi-tier perching layer houses can offer hens with perches, nests, and multi-level activity spaces. But their complex spatial structure and airflow configuration can also lead to local environmental differences and short-term fluctuations. It is often required for the accurate short-term prediction of indoor environmental parameters under proactive ventilation control and environmental risk warning in cage-free laying hen production. This study aimed to develop a short-term prediction for indoor temperature, relative humidity, and ammonia concentration in a multi-tier perching layer house, according to multi-point monitoring data. Continuous environmental data were collected from one experimental multi-tier perching layer house. The indoor monitoring points were arranged in different perching areas, while one outdoor monitoring point was used to represent boundary environmental conditions. Time alignment and resampling at a 20 min interval, point-wise mean values, and spatial ranges were calculated after data cleaning to evaluate environmental differences among monitoring points. Pearson correlation analysis was used to examine the relationship among temperature, relative humidity, and ammonia concentration. A short-term prediction framework was then developed using gradient boosting regression. Historical observations, outdoor environmental information, and indoor–outdoor difference features were used as model inputs for temperature and relative humidity. A multivariable prediction model was constructed to combine ammonia historical concentration with temperature and relative humidity. The most recent 7 d data was used as an independent validation set. In addition, recursive prediction was performed to evaluate the model performance for a future 24 h horizon. Furthermore, 0.05 and 0.95 quantile regression models were established to generate 90% prediction intervals. The results showed that the indoor environmental parameters differed among monitoring points. The average temperature, relative humidity, and ammonia concentration were 18.99-20.88 ℃, 46.52%-51.98%, and 1.32-1.86 mg/m³, respectively, at different indoor monitoring points. The mean spatial ranges were 2.31℃, 11.84%, and 1.27 mg/m³, respectively, indicating that the parameters varied among different perching areas. Correlation analysis showed that temperature and relative humidity were negatively correlated with ammonia concentration, with correlation coefficients of −0.26 and −0.16, respectively. Ammonia concentration was dominated by its historical state, thermal and humidity conditions, as well as ventilation. In the independent validation set, the temperature prediction model achieved a coefficient of determination of 0.96, a root mean square error of 0.73 ℃, and a mean absolute error of 0.50 ℃, whereas those values were 0.97, 2.94%, and 1.77%, respectively, in relative humidity. In ammonia concentration, the multivariable prediction model achieved a coefficient of determination of 0.76, a root mean square error of 0.44 mg/m³, and a mean absolute error of 0.27 mg/m³. In the multivariable model, the coefficient of determination increased from 0.64 to 0.76, whereas the mean absolute error reduced from 0.37 to 0.27 mg/m³, compared with the univariate gradient boosting regression model with only ammonia historical information. As such, temperature and relative humidity features provided useful supplementary information for ammonia prediction. The 24 h recursive prediction showed that the stable prediction performance was maintained during continuous forecasting. Smooth prediction curves were produced without outstanding abnormal jumps. The 90% prediction intervals were also provided to quantify the fluctuation range of environmental parameters. The short-term prediction can be used to adjust proactive ventilation for the less ammonia risk environment in multi-tier perching layer houses.

Surface litter manure can significantly increase the risk of diseases in broiler brooding houses, such as coccidiosis and colibacillosis. Indoor air quality can be deteriorated, due to the release of ammonia, hydrogen sulfide, and methane. However, existing double-crank shoveling-throwing mechanisms of surface manure cleaning have suffered from low efficiency, performance, and excessive vibration, because the key structural parameters are determined empirically without systematic multi-objective optimization. In this study, a multi-objective optimization was developed for the double-crank shoveling-throwing mechanism using an improved NSGA-II algorithm. Thereby, better performance was achieved to improve the shoveling efficiency, energy consumption, and stability. A kinematic and dynamic model of the double-crank shoveling-throwing mechanism was first established to reveal the influence of structural parameters on the shoveling trajectory and force behaviors. A coupled ADAMS-EDEM simulation model was then constructed to simulate the interaction between the mechanism and manure particles. As such, 60 sets of variable samples were generated after the optimal Latin hypercube sampling. A Gaussian process regression (GPR) surrogate model was constructed to map the relationship between six variables and the shovel throwing quality Q. The relative error between the prediction and simulation was 3.85%, indicating high prediction accuracy. An improved NSGA-II algorithm was proposed to overcome the limitations of the standard NSGA-II algorithm—namely, significant dimensional differences among the three objectives, highly nonlinear parameter-performance mapping, and discontinuous parameter space. Three improvements were introduced: (1) an adaptive normalization mechanism to eliminate dimensional effects; (2) a hybrid optimization framework with global search (NSGA-II) and local refinement (sequential quadratic programming, SQP) for the high convergence accuracy; and (3) an improved crowding distance and solution selection mechanism for the distribution uniformity of the Pareto front. The improved algorithm was compared with standard NSGA-II, NSGA-III, MOPSO, and MOEA/D, according to three performance indicators: Inverted Generational Distance (IGD), Spacing, and Hypervolume (HV). The results showed that the improved NSGA-II algorithm significantly outperformed the rest. Specifically, the IGD value decreased by 71%, 68%, and 64%, respectively, compared with standard NSGA-II, MOPSO, and MOEA/D. Spacing value decreased by 26%, compared with standard NSGA-II, whereas, the HV value increased by 16%. The better performance was achieved in the high convergence, more uniform distribution, and higher coverage of the true Pareto front. The optimal compromise solution was selected from the Pareto set using the entropy-weighted TOPSIS. The optimal parameters were recommended: l₁=70.4 mm, l₂=88.5 mm, l₃=100.2 mm, l₄=30.3 mm, b=99.7 mm, β=37.4°. A prototype was manufactured for the double-crank shoveling-throwing device, according to the optimal parameters. Field experiments were conducted in three repetitions in a brooding house in Zhouzhi County, Xi’an, Shaanxi Province, China, in December 2025. The experimental conditions were as follows: Litter layer with a thickness of 50–60 mm, chicken manure layer thickness of 10-20 mm, and manure moisture content of 30%–35%. The results demonstrated that the optimal mechanism improved shoveling efficiency by 138.24%, whereas the driving torque and the angular acceleration peak at the shovel end were reduced by 35.12%, and 35.60%, respectively. In addition, the residual rate of surface manure decreased from 18.45% to 8.13%, with a reduction of 55.99%, indicating significantly improved cleaning quality. A multi-objective optimization framework was provided for the double-crank shoveling-throwing mechanism using ADAMS-EDEM simulation, GPR surrogate modeling, and an improved NSGA-II algorithm. The dimensional differences and high nonlinearity were avoided for the low computational cost after engineering optimization. The improved NSGA-II algorithm demonstrated superior convergence and distribution performance, compared with mainstream multi-objective algorithms. The optimal mechanism was achieved to balance shoveling efficiency, energy consumption, and operational stability. The findings can offer a complete technical pathway to enhance the performance of hinge-type multi-bar mechanisms, particularly for manure cleaning equipment in the poultry industry.

The meat duck industry in China has contributed over 82% of the total slaughter volume worldwide. Therefore, an optimal air temperature is often required for the comfort and survival of meat ducks during breeding. However, the air temperature is susceptible to multiple factors, such as relative humidity and illumination intensity. It is a high demand to timely and accurately predict air temperature for the high-density healthy breeding. However, conventional temperature prediction has been limited to low accuracy, robustness, and generalization. In this study, a hybrid 1DCNN-BiLSTM-DQN model was proposed to integrate with a one-dimensional convolutional neural network (1DCNN), a bidirectional long short-term memory network (BiLSTM), and a deep Q-network (DQN). Duck-house temperature was accurately predicted after model construction. The temperature time-series signal was also decomposed into high- and low-frequency components via the discrete Fourier transform (DFT). Given that the high-frequency component represented short-term fluctuations, the 1DCNN was used to extract local features from the high-frequency component; whereas the low-frequency component represented long-term fluctuations, the BiLSTM was used to extract long-sequence dependency features from the low-frequency component. Subsequently, the two sets of features were fused using a concatenation model. And finally, the temperature prediction value was obtained after the mapping of a fully connected layer. Furthermore, the DQN algorithm was introduced to construct an agent for iterative optimization of the hyperparameters. An adaptive mechanism was optimized with an 8-dimensional state space and a 12-dimensional action space, enabling dynamic optimization of key hyperparameters, such as the learning rate, number of hidden layers, dropout rate, and network architecture. Thereby, the prediction robustness of the model was improved under scenarios of seasonal transitions and extreme weather. The indoor and outdoor temperature data of net-raised duck houses were collected in Gaoyou City, Yangzhou City, Jiangsu Province, from March 22, 2025, to March 22, 2026. The results demonstrated that the 1DCNN-BiLSTM-DQN model achieved a coefficient of determination (R²) of 0.993 with an optimal input step size of 48. The MAE and RMSE were superior to the conventional models, such as the Temporal Convolutional Network and Transformer. Specifically, the 1DCNN-BiLSTM-DQN model exhibited the following improvements under different weather conditions: On cloudy days, the MAE and RMSE decreased from 0.29 °C to 0.18 °C, and from 0.36 °C to 0.23 °C, whereas the R² increased from 0.77 to 0.91; On sunny days, the MAE and RMSE decreased from 0.48 °C to 0.23 °C, and from 0.59 °C to 0.31 °C, whereas the R² increased from 0.79 to 0.94; On rainy days, the MAE and RMSE decreased from 0.54 °C to 0.33 °C, and from 0.70 °C to 0.46 °C, whereas the R² increased from 0.72 to 0.89. The combined 1DCNN-BiLSTM prediction model achieved better prediction performance compared with the conventional models, such as BiLSTM, 1DCNN, TCN, and Transformer. In summary, the 1DCNN-BiLSTM-DQN model can be expected to predict the temperature in duck houses. The findings can also provide data support for early environmental regulation, thereby reducing the risk of environmental stress in meat ducks.

Ratoon rice, defined as the production of a second crop from the stubbles remaining after harvest of the main crop, was increasingly promoted in China as a strategy to enhance annual grain yield on limited arable land without additional land preparation, sowing, or transplanting. With the rapid adoption of combine harvesters in major rice-growing regions, mechanized rice ratooning technology with the main crop harvested mechanically became the predominant method in ratoon rice. However, mechanical harvesting introduced critical constraints to ratoon crop. Track-induced crushing, excessive ground pressure, and unstable cutting height frequently caused stubble breakage and axillary bud injury, thereby reducing bud survival, suppressing ratoon tiller production, and compromising yield formation and milling quality in the ratoon crop. These mechanical damages emerged as a primary bottleneck limiting the stable and large-scale adoption of mechanized rice ratooning system. From an agricultural engineering perspective, this review systematically synthesized recent advances in understanding damage mechanisms and in developing technical strategies for loss mitigation, yield stabilization, and quality improvement in mechanized rice ratooning system, with emphasis on machinery optimization, varietal improvement, and integrated agronomic management. Mechanistic studies demonstrated that ground contact pressure and shear forces generated by harvester tracks disrupted stubble structural integrity, damaged vascular tissues, and impeded assimilate translocation to regenerated buds. Under high soil moisture conditions, increased sinkage and soil deformation amplified mechanical stress, substantially reducing bud sprouting rates within track zones compared with non-track areas. In crushed zones, ratoon development often shifted from upper-node buds to lower-node buds with delayed phenology, resulting in reduced canopy uniformity, decreased effective panicle number, and significant yield penalties. Delayed panicle emergence in track areas also led to asynchronous maturity within fields, thereby decreasing head rice rate and increasing variability in milling quality. To quantify mechanical damage, studies adopted indicators including missing-stubble rate, bud survival rate, ratoon tiller-to-panicle conversion rate, yield loss rate, head rice rate, and chalkiness-related parameters. Emerging technologies such as unmanned aerial vehicle remote sensing, machine vision, and in-field sensors were increasingly applied to identify track zones and characterize spatial heterogeneity of mechanical damage, although standardized evaluation protocols remain insufficient. Engineering innovations primarily targeted reductions in crushed area and crushing intensity, as well as improvements in stubble-height uniformity. Advances included lightweight chassis designs with reduced ground pressure, optimized track width and cutting width configurations, automatic header-height control based on multi-sensor perception systems, stubble-righting devices integrated with harvesters, and navigation-assisted path planning to minimize track overlap and headland damage. Although these technologies effectively mitigated mechanical impact and improved ratoon crop performance under experimental conditions, trade-offs among ground pressure, machine stability, fuel consumption, operational cost, and field adaptability limited widespread commercial application. Varietal differences in stem mechanical strength, ratooning ability, and non-structural carbohydrate reserves significantly influenced tolerance to mechanical harvesting stress. Evaluation metrics extended beyond ratoon yield to include bud survival rate, ratoon panicle number, stem morphological traits, and biomechanical properties. Although quantitative trait loci and candidate genes associated with ratooning ability and stem strength were reported, stable loci and deployable molecular markers specifically targeting crushing tolerance remain limited. Complementary agronomic practices, including skip-row planting, pre-harvest drainage to enhance soil bearing capacity, timely post-harvest nitrogen topdressing with balanced phosphorus and potassium inputs, and targeted rehabilitation of track zones, partially alleviated yield and quality losses. Overall, the accumulated evidence indicated that future progress in mechanized rice ratooning system depended on coordinated machine, variety, and agronomy integration, lightweight and intelligent harvester development, standardized damage evaluation systems, and digital monitoring platforms to ensure stable, scalable, and quality-oriented ratoon rice production.

High-load disturbances during rotary tillage can cause significant wheel slip on the electric-drive mobile platform in the distributed horticulture facility. It is often required to control the speed-slip rate for the longitudinal stability of the distributed horticultural facility. In this study, a cascaded controller of vehicle speed–slip rate was proposed using integral robust vehicle speed and sliding-mode slip rate control. Its effectiveness was validated using simulations and vehicle experiments. Firstly, a dynamic model was established for the coupled system between the distributed electric-drive horticultural platform and the rotary tiller. Tire-soil interaction and the resistance of rotary tillage were also considered to explicitly incorporate the wheel rotational dynamics and external disturbance torques. Soil adhesion also led to variable tillage resistance. Moreover, a coupled modeling framework was constructed to describe the nonlinear relationship between longitudinal tire force and slip ratio. The traction generation was accurately characterized under deformable soil conditions. The slip regulation and speed stabilization were coordinated under high-load environments. Subsequently, an outer-loop vehicle speed controller was designed to incorporate integral robust control. The steady-state errors were eliminated from the operational disturbances for the high-speed stability. The integral term was used to compensate for the persistent disturbance-induced bias. While the robust component was enhanced, the controller’s tolerance to parametric uncertainties and unmodeled dynamics. Integral action was combined with robustness enhancement. The outer-loop controller maintained accurate speed tracking, even when sudden load fluctuations occurred. Furthermore, an inner-loop slip rate controller was developed using sliding-mode control. The optimal slip rate was obtained from the inverse tire model to serve as the reference input for the rapid convergence and precise tracking of slip rate. Sliding-mode control was selected for its high robustness against disturbances and modeling uncertainties, thereby enabling the dynamic response and strong anti-interference. The optimal slip rate corresponded to the traction peak region of the tire–soil interaction curve. Traction efficiency was maximized to prevent excessive slip. The outer and inner loops were coordinated for the longitudinal stability of the platform under high disturbance. A control strategy was then integrated for anti-slip driving and speed regulation. Specifically, the inner loop was used to rapidly suppress the deviations of the slip ratio, while the outer loop was for the global speed regulation using a cascaded structure. A hierarchical architecture of traction control was constructed, suitable for the distributed electric-drive systems. Simulation results indicate that the cascaded controller achieved an average speed error of 0.10 km/h under sudden muddy conditions, which was reduced by 16.6% and 67.7%, compared with the switching and speed control, respectively. The speed recovery time was 0.11 s, which was reduced by 64.5% and 68.6%, respectively. There was an average speed error of 0.07 km/h under variable tillage depths, which was reduced by 40.0% and 52.0%, compared with switching control and speed control, respectively. Experimental results indicate that an average speed error of 0.44 km/h was found under acceleration, which was reduced by 4.3% and 8.3%, compared with the switching and speed control, respectively. The average slip ratio was 0.15, which was reduced by 11.7% and 16.6%, respectively. There was an average speed error of 0.20 km/h under deep tillage, which was reduced by 20.0% and 37.5%, respectively. The average slip ratio was 0.13, which was reduced by 23.5% and 31.6%, respectively. Therefore, the cascaded controller can be expected to effectively suppress the slip ratio during rotary tillage, thereby enhancing the speed control performance and operational stability.

Asparagus harvesting can be confined to the efficacy of robotic vision in recent years. Asparagus spears are characterized by a slender morphology in their natural growth state. These tender stems are highly prone to mutual occlusion and overlapping when growing densely in field conditions. Furthermore, the stout mother stems can simultaneously present as the complex background interference. Collectively, it is often required for the high accuracy of the multi-target segmentation and recognition using machine vision. In this study, the lightweight instance segmentation model (YOLO11n-seg) was adopted as a baseline, in order to improve the precise positioning and harvesting performance of the robotic end-effector. Consequently, an optimized model named YOLO11n-SAL was also proposed to specifically tailor the slender, occluded targets with high fidelity. Two modules were introduced to enhance the feature extraction and attention mechanisms in the architectural framework. Firstly, the multi-scale edge enhancement Module (MEEM) was conceptually designed and integrated in order to mitigate the challenge wherein the edge features of the slender asparagus targets were inherently weak and easily lost during convolutional operations. Multi-scale decomposition was performed on the convolutional feature maps. The MEEM effectively extracted and intensified the edge and contour information before feature fusion. The sensitivity to the target boundaries was significantly elevated for the high segmentation precision, thereby enhancing the perceptual capability of the targets with the slender morphological structures. Secondly, the separated and enhancement attention module (SEAM) was introduced to rectify the feature confusion and data incompleteness caused by inter-target occlusion. Attention separation over both channel and spatial dimensions was also utilized to adaptively perceive the local and global features of the occluded asparagus at the varying scales. These features were selectively enhanced and effectively fused to better position the visible subjects of the partially masked targets, while suppressing the background noise and distractor information. The robust performance of the detection and recognition was maintained even within the complex and cluttered environments. A series of experiments was conducted to verify the effectiveness of the improved model. Quantitative evaluation results indicate that the improved YOLO11n-SAL model achieved significant gains over all key performance indicators, compared with the baseline model. In the detection task of the target bounding box, the superior performance was achieved with a detection precision of 94.2%, a recall rate of 83.1%, a mean average precision at IoU threshold 0.5 (mAP_0.5) of 91.2%, and a mean average precision at IoU threshold 0.5-0.95(mAP_0.5-0.95) of 76.2%. In the more granular instance mask segmentation, the model also performed impressively. The segmentation precision, recall, mAP_0.5 and mAP_0.5-0.95 reached 93.4%, 77.9%, 90.7%, and 62.7%, respectively. Furthermore, the heatmap analysis demonstrated that the YOLO11n-SAL model was markedly improved to perceive the asparagus edge features over different scenarios, with the superior multi-target segmentation and recognition under occluded conditions. The high accuracy of the segmentation and recognition was achieved to reduce the interference in the complex multi-scenario environments, compared with the baseline. Finally, a series of asparagus recognition, positioning, harvesting, and grasping trials were carried out using depth cameras and mechanical arms, in order to validate the cognition and position performance in the actual deployment scenarios. The empirical results showed that a positioning success rate of not less than 90% was accompanied by effective harvesting and grasping performance. These findings can provide reliable technical support for the advancement of robotic harvesting in precision agriculture.

Conventional Miner’s linear cumulative damage can focus only on the stress amplitude. In this study, a dynamic damage assessment was proposed for the high-horsepower tractor using the measured load spectrum. The research object was taken as the transmission shaft of a 162 kW four-wheel drive wheeled tractor. Multidimensional parameters were integrated to employ a dynamic weight allocation. A framework of the damage assessment was also established using the loads in the time domain, energy distribution in the frequency domain, and fluctuation features. Firstly, a torque test was developed using the transmission shaft of a tractor. The high-precision strain torque sensors were integrated with the wireless data acquisition. Torque fluctuation signals were captured under various conditions in real time, such as field operations and road transportation. The wheel torque signals were measured in the field, and then converted into the equivalent torque at the transmission shaft, according to the wheel-side planetary reduction mechanism, central transmission, transfer case, creeper gear, and gearbox. Subsequently, the rainflow counting was applied to identify the load cycles in the torque data. Statistical features of the loads were derived from high-confidence data for subsequent analysis. A composite model of the "three-dimensional linear weighting–nonlinear compression" was proposed to construct the damage indicator. Furthermore, three indicators—load gradient, frequency-band damage energy, and load kurtosis—were linearly weighted. The weighting parameters of these three indicators were optimized using a grid search algorithm. The root mean square error and absolute percentage error were employed to quantitatively evaluate the performance of different parameter combinations. The optimal weight allocation was determined as 0.350, 0.425, and 0.225. An S-shaped compression mapping with the sigmoid function was introduced to mitigate the influence of extreme values on the assessment, effectively balancing the sensitivity and robustness. The center of the load cycle time was indexed within the intensity index window of the dynamic damage. Time-frequency features were integrated into the cyclic damage for the amplitude correction. A hazardous threshold with the dynamic damage intensity index represented the exponential amplification of the cyclic amplitudes. The Goodman formula was then used to convert the torque signals into equivalent zero-mean stress amplitudes. And the S-N curve of the transmission shaft was applied to compute the damage. As such, a systematic assessment was realized on the dynamic damage. Experimental results demonstrate that the improved model effectively calculated the damage over the load bands. The peak normalized power spectral density energy occurred at 0.1 Hz, while the maximum damage frequency band was at 0.43 Hz, with a damage value of 1.78×10^-8. There was no coincidence between the peak power spectral density energy and the maximum damage. The theoretical correctness of the improved model was validated because the conventional approach only considered the amplitude. The relative error of the conventional Miner’s damage rule was 51.11%, whereas the relative error of the damage prediction was only 0.67%. The high accuracy and feasibility exhibited in the damage prediction. The linear damage models were advanced beyond the loading sequence and statistical analysis, thus providing the theoretical basis and insights for future assessment of the damage. Furthermore, a three-dimensional space of the damage feature was incorporated with the load gradient, frequency-band energy, and load kurtosis. The multidimensional parameter mechanism can be directly applied to the life prediction of the rotating machinery, such as the transmission shafts and gearboxes, or the damage assessment in the structural domains, such as the composite materials and additive manufacturing components. The finding can provide a theoretical and quantitative tool for the intelligent operation and prediction.

Wheel-type rice transplanters have been widely used in mechanized paddy-field transplanting because of their flexible operation and convenient field transfer. However, during paddy-field operation, the unstable adhesion state between the driving wheels and saturated paddy soil may induce lateral deviation, longitudinal slip, and row-spacing errors. To clarify the slip behavior of a wheel-type rice transplanter under different soil moisture contents, traveling speeds, and whole-machine masses, this study analyzed the variation characteristics of the slip ratios of a wheel-type rice transplanter. A Xinyang 2ZG-6D1(G4) wheel-type rice transplanter was selected as the research object. A whole-machine multi-body dynamics model was established in RecurDyn, and a layered paddy-soil particle bed was constructed in EDEM. The Hertz-Mindlin with JKR contact model was adopted as the soil contact model to describe the adhesive contact behavior among paddy-soil particles and between soil particles and soil-engaging components. Compression tests and direct shear tests were conducted to calibrate the discrete element parameters of paddy soil, thereby improving the reliability of the soil model. The bidirectional coupling between the transplanter model and the soil model was realized through the EDEM-RecurDyn coupling interface. Soil moisture content, traveling speed, and whole-machine mass were selected as experimental factors, while lateral slip ratio and longitudinal slip ratio were used as evaluation indexes. Based on the calibrated discrete element method and multi-body dynamics (DEM-MBD) coupling model, a Box-Behnken response surface experiment was conducted to investigate the effects and interaction mechanisms of the three factors on the two slip indexes. Regression models of the lateral and longitudinal slip ratios were established, and multi-objective optimization was carried out to obtain a suitable parameter combination. Field tests were finally performed to verify the prediction accuracy of the coupled simulation model. The results showed that soil moisture content had the most significant effect on both lateral and longitudinal slip ratios, with contribution rates of 20.54% and 35.74%, respectively. The interaction between soil moisture content and whole-machine mass also had an obvious influence on the two slip ratios, indicating that the effect of machine load on wheel-soil interaction depended strongly on the moisture state of paddy soil. With increasing soil moisture content, the bearing, shear, and adhesion characteristics of the soil changed, further affecting wheel sinkage, soil adhesion, and driving stability. Within the experimental range, when the soil moisture content was 32%, the traveling speed was 0.54 m/s, and the whole-machine mass was 854 kg, the lateral and longitudinal slip ratios showed relatively good comprehensive performance; that is, both indexes remained at relatively low levels under the multi-objective optimization constraints. The field validation results showed that the measured lateral slip ratio was 1.55%, while the simulated value was 1.36%, with a relative error of 12.25%. The measured longitudinal slip ratio was 11.48%, while the simulated value was 10.15%, with a relative error of 11.59%. Both errors were within 15%, indicating that the established DEM-MBD coupling model can reasonably predict the slip ratios of a wheel-type rice transplanter under paddy-field conditions. This study provides a feasible simulation method for analyzing wheel-soil interaction in paddy fields and offers a reference for the design of walking systems and the optimization of operating parameters for paddy-field machinery.

Robotic arms are often required for high picking efficiency, path planning, and path smoothness for safflower harvesting in unstructured environments. This study aims to introduce a picking area clustering and a Target redirecting rapidly-exploring random tree (TR-RRT*) path planning. Firstly, a roller end-effector was designed with an effective picking area of 5 cm × 20 cm, according to the spatial distribution of safflower seed balls under natural conditions. A picking point clustering was also developed to divide the working range of the robotic arm into multiple sub-areas. Each cluster was designed to cover 1-3 picking points. The end-effector was used to harvest 1-3 safflowers in a single operation. A goal redirecting, a goal-direct and deflection expansion, and an artificial potential field (APF) tangential escape force strategy were integrated into the bidirectional RRT* framework to improve the path search efficiency and obstacle avoidance. Among them, the goal redirecting strategy continuously updated the target points during expansion to rapidly connect paths within opportunity windows. The goal-directed and deflection expansion allowed both search trees to extend from the nodes closest to the goal, thus maximizing progress toward the target while deflecting to avoid obstacles. The tangential escape force strategy introduced a tangential component into the conventional artificial potential field, enabling smooth sliding along obstacle boundaries when approaching them. As such, the module effectively avoided the path oscillation and target unreachability in the conventional APF. In path optimization, a combination of a greedy jump-point strategy, interpolated curvature optimization, and B-spline curve fitting was applied to smoothly adjust the curvature of path nodes and then generate high-order continuous trajectories. An obstacle avoidance reconstruction was also introduced to prevent the trajectory penetration through obstacles for the continuous, collision-free, and smooth motion of the robotic arm. A comparison was made on the TR-RRT* and seven algorithms in the dense, small-obstacle environments. The results demonstrated that the superior performance was achieved in the complex obstacle environments, where the path lengths (3892.05mm) were shortened by 9.16% and 8.22%, compared with the IBI-P-RRT*(4284.44 mm) and BI-RRT*(4240.45 mm), respectively; While the average planning time (0.30 s) was only 7.94% of that of RRT*(3.78 s) and 69.77% of BI-RRT*(0.43 s), respectively. Additionally, the steering angles of BI-RRT*(37.51°) and BI-APF-RRT*(39.61°) were 1.22 and 1.29 times larger than those of the improved algorithm (30.63°), indicating significant improvement of the path smoothness. Statistical quantitative experiments were conducted for the algorithm in different obstacle environments. The optimal performance was achieved in both path length and time consumption under various environments. Moreover, the TR-RRT* algorithm successfully planned paths in narrow passage obstacle environments. The tendency of conventional APF was to avoid the local oscillations during path planning in such scenarios. Physical harvesting tests further validated the effectiveness of the clustering and TR-RRT* algorithm. The robotic arm took an average of 3.64 s to move from the initial position to the first target point, with an average transfer time of 3.12 s between tasks. The average positional error relative to the robotic arm's workspace was less than 0.9%, indicating the stable and efficient performance of safflower harvesting.

Ecological ditch–pond systems are important measures for controlling agricultural non-point source pollution, yet their practical application is constrained by unstable purification performance and large land occupancy. The effective application of such systems in irrigation districts depends not only on the design and operation management of individual units, but also significantly on their spatial layout (including system area and unit connection pattern). Existing studies often fail to adequately capture the multi-level dynamic responses of water volume and water quality in such systems, which hinders their support for spatial layout optimization. Against this backdrop, this study proposes a system dynamics-based simulation method for optimizing the spatial layout of ditch-pond systems in irrigation districts. A field-ditch-pond system model was developed using the system dynamics simulation tool Vensim, integrating water balance, pollutant removal processes, and hydraulic connections among ditches and ponds. The water depth in the paddy model was determined by inflows, outflows, and water consumption during each time step, while the total nitrogen and total phosphorus concentrations were simulated by considering fertilization, first-order pollutant decay, and inputs from rainfall and irrigation. For the ditch–pond unit model, water volume changes were governed by rainfall, evapotranspiration, seepage, upstream inflow, and drainage discharge. Pollutant concentrations in the ditch–pond unit model were calculated using two modes: static storage-based reduction and dynamic drainage-based reduction. The paddy and ditch–pond unit models were linked through system dynamics into an integrated field-ditch-pond system model, which was calibrated and validated using field monitoring data. A case study was conducted in a typical double-cropping paddy high-standard farmland demonstration area in southern China. The model verification results show that the developed model can effectively simulate the dynamic variations of water volume and pollutant concentrations in the system. The case analysis results show that: 1) With increasing ditch-pond to paddy area ratio, nitrogen and phosphorus removal rates rise, but the rate of increase gradually slows down, suggesting an optimal range of 5%-9%; 2) During the late rice season or under larger area ratios, concentrating wetlands in a single drainage path results in a significantly lower removal rate compared to other layouts; 3) For practical implementation, it is recommended to first determine an appropriate area ratio based on target pollutant reduction goals. Subsequently, wetlands may be placed either at the main drainage outlet or distributed in parallel across different drainage pathways, depending on site-specific conditions. The findings provide a methodological reference for modeling multi-level wetland systems and offer a scientific basis for ecological control of agricultural non-point source pollution. Future work may further refine the simulation of water cycling and pollutant transformation processes within field-ditch-pond systems to enhance model accuracy and applicability.

Cropland water–land balance relationship can be regulated to maintain the cropland productivity, ecological stability, and sustainable land use. It is often required to optimize cropland spatial layout and crop planting structure in sustainable water and land resources, according to “Determining Land Use by Water Availability”. In this study, an integrated analytical framework was developed to explore the spatiotemporal characteristics of the balance between water and cultivated land resources. There was also a close relationship between crop water requirement, water deficit and surplus, drought events, and productivity loss. The research area was taken as the five major agricultural regions in northern China. Meteorological, crop planting structure, and gross primary productivity (GPP) were monthly collected from 2001 to 2022. The full-growing-season crop water requirements of winter wheat, spring maize, and summer maize were estimated using the FAO-56 crop coefficient. A decadal Crop Water Deficit and Surplus Index (CWDI) and its standardized form, the Standardized Crop Water Deficit and Surplus Index (SCWDI), were then constructed to integrate run theory. Empirical orthogonal function (EOF) analysis, correlation analysis, and a Copula–Bayesian conditional probability model were used to systematically identify the Spatiotemporal evolution of cropland water–land balance relationships, crop critical water-demand periods, and productivity loss risks under drought stress. The results showed that (1) multi-year mean full-growing-season water requirements of winter wheat and maize were 500 and 594 mm, respectively, particularly with 627 and 536 mm for spring and summer maize, respectively. Crop water requirements shared significant regional differences. In winter wheat, the water requirement followed the descending order of Huang–Huai–Hai Plain (521 mm) > Loess Plateau (514 mm) > Gansu–Xinjiang Region (467 mm), while spring maize shared the highest water requirement in the Gansu–Xinjiang Region (662 mm). (2) Average drought-event frequency ranged from 0.4 to 2.0 events per year. The Gansu–Xinjiang Region was characterized by a high proportion of extreme drought events, long duration, and high intensity, indicating a cumulative drought pattern, whereas the Huang–Huai–Hai Plain was dominated by high-frequency but short-duration drought events. (3) EOF decomposition showed that the first five modes cumulatively explained 73.7% of the total variance, with EOF1 and EOF2 accounting for 28.6% and 20.0%, respectively, indicating region-wide consistency and regional heterogeneity in interannual dry–wet variations of cropland. (4) Correlation analysis between SCWDI and standardized GPP (SGPP) showed that the key months were identified as July for spring and summer maize, while May for winter wheat. According to the cumulative effect, the water-demand stages of spring maize, summer maize, and winter wheat corresponded to May–June, June, and March–April, respectively. (5) SCWDI–SGPP dependence structures of spring maize and summer maize were best fitted by the Gaussian Copula, while the Frank Copula performed best for winter wheat. The average probabilities of productivity loss were 63%, 80%, and 44%, respectively, for spring maize, summer maize, and winter wheat under extreme drought conditions. The north-central Huang–Huai–Hai Plain and the Loess Plateau were identified as the drought stress conversion into productivity loss. The finding can provide a scientific basis for the optimal cropland layout and crop structure during drought risk prevention in northern China.

Near-surface water vapor condensation is one of the most crucial steps to fully utilize atmospheric water sources in ecological agriculture. It is often required to clarify the dynamic relationship between near-surface water vapor and condensation. This study aims to investigate near-surface water vapor dynamics and their response to condensation events in arid regions. Three geographical conditions of northwest China were selected to capture the meteorological parameters, including the southeast margin of the Tengger Desert (TD), the arid belt of the center in Ningxia Hui Autonomous Region (CANX), and the semi-arid region in Ningxia Hui Autonomous Region (SANX). The hydrostatic integration was employed to calculate water vapor flux and content within a 100 m range above ground at each observation point, based on the evolution patterns of meteorological factors with hight at the Yinchuan radiosonde station. The leaf wetness sensor of PHYTOS31 was used to calculate the condensation water amount at 5 cm above ground. A correlation analysis was performed on the water vapor, condensation water, and meteorological parameters. The results indicate that there were significant differences in annual total condensation water at TD, CANX, and SANX sites (P<0.05), with annual averages of 13.35, 22.68, and 32.80 mm, respectively, during the observation period. Spatiotemporal variations in water vapor flux and content were significant (P <0.05) at all three stations, thus peaking in summer and declining in winter. Minimum monthly water vapor flux at TD, CANX, and SANX were 1.9, 2.3, and 1.8 kg/(m·s), respectively, while minimum monthly water vapor content was 1.30, 1.45, and 1.60 mm, respectively. Peak water vapor flux and content occurred in July at SANX, and in August at TD and CANX. Water vapor flux was markedly higher at the southeast margin of the Tengger Desert and arid region than that in the semi-arid regions, with 21.2, 19.4, and 13.9 kg/(m·s) for the maximum TD, CANX, and SANX, respectively. The monthly average water vapor content was highest at the SANX site (13.69 mm), while those were 11.35 and 11.23 mm, respectively, at the TD and CANX sites. The primary wind direction ranges influencing water vapor flux and content at the three stations were: TD with 0°–60° and 150°–210°, CANX with 180°–240°, and SANX with 0°–30°, 120°–240°, and 300°–359°. In terms of a single condensation event, both water vapor flux and content decreased during the condensation accumulation phase, whereas there was an increase when the condensation dissipated. Condensation content showed a significant negative correlation with water vapor content (P<0.05). The correlation coefficients for TD, CANX, and SANX were −0.652, −0.751, and −0.722, respectively. Water vapor flux first decreased and then increased during the diurnal cycle without condensation, due to the absence of water vapor phase change. While water vapor content shared an increasing trend. Water vapor flux and content exhibited a significant negative correlation (P<0.05) during the process. These findings can also provide valuable insights to characterize near-surface water vapor dynamics under diverse geographical conditions.

Water scarcity has long constrained agricultural sustainability in the Huang-Huai-Hai Plain, a vital grain production base in China. Regional water resources can also be regulated to improve water use efficiency in sustainable agriculture. It is often required to precisely assess agricultural water use efficiency. Crop production water footprint can be expected to measure the sustainability and efficiency of water resource utilization during the entire crop growth cycle. This study selected winter wheat as the research subject. Assimilated variables were utilized as remotely sensed leaf area index (LAI) and soil moisture (SM). A quantitative assessment was also developed for winter wheat water footprint, according to dual-variable assimilation of crop models and remote sensing data. Spatial dependency and clustering of winter wheat water footprint were then determined using spatial autocorrelation analysis. Furthermore, winter wheat yield–total water footprint quadrant classification, blue and green water resource dependency, and groundwater extraction proportion were integrated to clarify regional water source dependence and formulate differentiated water footprint management strategies. The results indicated that: 1) Data assimilation significantly improved the accuracy of the WOFOST model to simulate the winter wheat yield. There was strong consistency between the simulation and the statistical yield after data assimilation, with an R² increased to 0.98 and an RMSE reduced to 67.68 kg/hm². The accuracy significantly also improved after simulation, compared with an R² of 0.42 and an RMSE of 566.78 kg/hm²; 2) The average green, blue, and total water footprint of winter wheat were 0.35, 0.30, and 0.65 m³/kg, respectively, after data assimilation. The green and the total water footprint exhibited a spatial distribution pattern higher in the south and lower in the north, while the blue water footprint showed a pattern higher in the north and lower in the south; 3) Spatial autocorrelation of winter wheat green and blue water footprint was stronger than that of the total water footprint. The blue, green, and total water footprint of winter wheat exhibited significant spatial clustering, primarily characterized by high-high and low-low clustering; 4) The northern region should prioritize stable production, water saving regulation, and reduction of groundwater extraction, whereas the southern region should focus on improving precipitation use efficiency. This finding can provide scientific support and decision-making basis for the refined and differentiated water resource strategies in typical water-scarce agricultural regions, such as the Huang-Huai-Hai Plain. A solid theoretical foundation and technical framework can help allocate agricultural water resources at the regional scale.

Recognition accuracy of small targets is often required for dense fruit distribution in natural orchard environments. Therefore, a flat peach detection model based on an improved YOLOv8 architecture, termed CCGs-YOLO, was proposed in this study. The proposed model integrates the MetaFormer framework with a convolutional gated linear unit module. A hybrid module combining convolution and attention mechanisms was introduced to enhance spatial feature extraction and improve feature representation under complex background conditions. Meanwhile, a channel-aware module was incorporated to simulate inter-channel dependencies, thereby improving the discrimination capability between fruit targets and cluttered backgrounds. Specifically, the C2f_ConvFormer module was employed to simultaneously capture local and global contextual information, while the C2f_CaFormer module was introduced to enhance channel interaction and feature aggregation. In addition, a convolutional gated linear unit mechanism was embedded into the network to improve feature selection capability and robustness against background noise. To address the small object detection problem in densely distributed fruit scenarios, localization accuracy was further improved. An optimized regression loss function based on an inner-overlap constraint, named Inner-CIoU, was adopted to achieve more accurate bounding box regression and reduce localization errors caused by overlapping targets. Experimental results demonstrated that the ConvFormer module improved the F1-score to 90.44% and the mAP50 to 96.40%, indicating enhanced feature extraction capability. The CaFormer module increased the F1-score from 89.96% to 90.63%, while the mAP50 further improved to 96.12%, demonstrating effective channel modeling capability under relatively high inference efficiency. When only the convolutional gated linear unit mechanism was applied, the F1-score reached 90.20% and the mAP50 achieved 96.12%, verifying its effectiveness in enhancing feature representation. Furthermore, the combination of CaFormer and convolutional gated linear unit improved the Precision to 91.47%, the F1-score to 90.77%, and the mAP50 to 96.24%, demonstrating the complementary advantages of channel modeling and gated feature selection. In terms of localization performance, Inner-CIoU improved both mAP and model convergence stability compared with the conventional CIoU loss function. After integrating all improved components, the model achieved relatively better overall performance. Precision reached 93.07%, representing an increase of 3.31 percentage points compared with the baseline model. The F1-score reached 90.87%, while the mAP50 achieved 96.24%. Meanwhile, the model size was reduced from 5.97 MB to 4.88 MB, and the number of parameters decreased from 3.01 million to 2.42 million, indicating that the proposed model possesses favorable lightweight characteristics while maintaining relatively high inference speed of 362.07 FPS. In addition, comparative experiments were conducted with several mainstream models, including different versions of the YOLO series. Under challenging scenarios such as occlusion, small targets, dense distribution, and edge targets, the proposed model achieved relatively superior performance in terms of Precision, F1-score, and mAP, demonstrating improved detection stability and feature perception capability. Visualization analysis further indicated that the improved model could focus more accurately on fruit regions and suppress background interference to a certain extent. Furthermore, deployment experiments on edge computing devices demonstrated that the proposed model could still maintain relatively high detection accuracy and stable performance under practical application conditions, with the mAP50-95 reaching 88.82%, indicating potential for real-world applications. Overall, the proposed model effectively balanced detection accuracy, model lightweight characteristics, and computational efficiency, demonstrating good robustness and adaptability in complex orchard environments. The proposed approach can provide a feasible technical solution for rapid and accurate fruit recognition in flat peach harvesting.

Fall armyworm (Spodoptera frugiperda) is one of the most serious pests in maize fields. It is often required to early and accurately detect its infestation for timely and effective pest prevention using unmanned aerial vehicle (UAV) imagery. However, reliable detection has been confined to the challenges: 1) The small and subtle feeding marks caused by the larvae, leading difficult to identify at high altitudes. 2) Consistent recognition has been limited to significant variations in object scale at different flight heights. 3) The accurate detection has also been limited to the low contrast between damaged leaf tissue and surrounding healthy foliage, especially under the different lighting and environmental conditions in fields. Collectively, advanced computer vision is necessary to robustly identify early signs of infestation at diverse scales under complex backgrounds. In this study, a robust deep learning model was developed to reliably identify the subtle infestation traces in multi-scale UAV images. A detection architecture, termed coordinated-BiFPN-P2-YOLO (CBP-YOLO), was also proposed using YOLOv8. Real-enhanced super-resolution generative adversarial network (Real-ESRGAN) was applied as a preprocessing step to reduce image degradation from low ground sampling distance. High-fidelity textures of leaf damage were reconstructed from original low-resolution inputs. The backbone of YOLOv8 was enhanced with the Coordinated attention (CA) mechanism. Spatial and channel-wise features were captured to improve the localization and discrimination of minute lesions. Furthermore, the neck component was upgraded with a Bi-directional feature pyramid network (BiFPN) for the highly efficient top-down and bottom-up cross-scale feature fusion. Information loss was minimized for consistent representation during hierarchical propagation at different scales. In addition, a detection head was added to specifically strengthen sensitivity to small targets, particularly at a 160×160 spatial resolution with 64-channel output. The improved model was trained and then evaluated on the custom UAV dataset, which was collected from maize fields naturally infested by fall armyworm under diverse lighting conditions and flight heights. Extensive experiments demonstrated that the CBP-YOLO achieved a peak performance on the imagery with a ground sampling distance (GSD) of 0.38 cm per pixel. Real-ESRGAN significantly alleviated texture blurring and edge ambiguity in low-resolution images, leading to better delineation of feeding scars. Ablation studies were conducted to evaluate the effectiveness of the improved model. There was an outstanding performance on the UAV multi-scale blade dataset. Specifically, there was an average precision (AP@0.5) of 76.5%, which increased by 3.4 percentage points, compared with the baseline model. The robustness and practical applicability of the improved model were obtained in the blades of varying scales during aerial inspection. A comparison showed that the CBP-YOLO outperformed state-of-the-art detectors—including YOLOv9 medium, YOLOv10 medium, YOLOv11 medium, Faster region-based convolutional neural network, and RetinaNet—by margins of 10.1, 7.2, 5.1, 9.3, and 17.9 percentage points in AP@0.5, respectively. Notably, the high precision was also maintained under varying illumination and partial occlusion, indicating strong generalization in agricultural environments. The improved CBP-YOLO framework effectively detected subtle, multi-scale fall armyworm infestation signals during UAV monitoring. Superior accuracy and robustness of the improved model were achieved to synergistically combine super-resolution enhancement, attention-aware feature extraction, fine-grained detection heads, and bidirectional multi-scale fusion. These findings can also provide a practical and scalable solution for early pest outbreak detection, thereby enabling timely intervention to reduce the crop losses in large-scale maize production.

Quantitative evaluation standards are often required to accurately predict sprouting regeneration, particularly for cutting quality. However, the coppicing surface targets cannot be recognized in complex fields during Caragana korshinskii shrub coppicing in the arid regions of Northwest China. In this study, a quantitative evaluation was proposed for sprouting regeneration, according to the synergistic association between cutting morphologies and agronomic traits. Accurate recognition of coppicing surfaces was achieved in unstructured field environments. A segmentation network was constructed, named WoodGrainNet. Firstly, the global context modelling and target localization were enhanced via a semantic stream using a lightweight Transformer architecture. Secondly, a frequency-domain stream was incorporated with the Haar discrete wavelet transform to effectively suppress abiotic background interference for the texture representation of the targets. Simultaneously, a shape stream was combined with a differentiable Sobel operator. Explicit constraints were also applied to high-frequency gradient regions. Thereby, the adjacent cut boundaries were delineated to effectively alleviate the adhesion of dense targets. Three-stream features were synergistically simulated for the deep integration. The WoodGrainNet significantly improved the segmentation robustness and instance discrimination of the improved model in complex scenes, particularly at the level of underlying physical feature representation. Accurate segmentation was achieved to automatically extract a key geometric phenotypic indicator for coppicing quality—coppicing surface circularity (C). Accordingly, the coppicing quality grading and sprouting prediction were established after evaluation. The experimental results demonstrate that the better performance of WoodGrainNet was achieved to balance high accuracy and real-time processing, with a mean Intersection over Union (mIoU) of 86.99% and an inference speed of 51.78 frames/s. The performance was significantly superior to mainstream networks, such as DeepLabV3+, effectively recognizing tiny coppicing targets. In terms of agronomic analysis, statistical results revealed that there was a significant positive correlation between the morphological quality of the coppicing surface and the sprouting potential of lateral branches. Notably, the coefficient of determination (R²) between the coppicing surface circularity (C) and the number of lateral branch sprouts reached 0.764 at the 5th week (N). The circularity served as an important morphological indicator to evaluate coppicing quality and sprouting regeneration. Field tests were conducted to verify its feasibility for engineering applications. The improved model was deployed on a mobile intelligent system. A discrimination accuracy of 90.8% was achieved for coppicing quality grades under mobile working conditions. Sprouting prediction trend was highly consistent with the measured ones, indicating its potential to replace manual inspection and subjective empirical judgment. The pixel-level phenotypic analysis of the coppicing surface was effectively transformed to predict the single-plant regeneration. The finding can provide a theoretical basis and technical support for the quality evaluation, parameter monitoring, and precise detection of ecological shrub forests in arid regions.

Fruit-tree canopies are typically characterized by complex branching architecture and dense foliage, leading to severe self-occlusion, uneven light distribution, and low light-use efficiency. However, experience-driven decisions cannot fully meet the large-scale pruning of the standard canopy shapes and optimal regulation of tree vigor in the orchard. In this study, an intelligent pruning model was proposed to optimize the canopy structure reconstruction and light-efficiency evaluation using Neural Radiance Fields (NeRF). A reproducible, quantitative, and visualization-friendly workflow was also provided for canopy analysis and pruning under a real orchard. Qingcuili plum (Prunus salicina cv. ‘Qingcuili’) was selected as the target species. Multi-view videos were captured around each tree from multiple angles for sufficient coverage of the canopy under field lighting and background. A NeRF reconstruction was used to learn volumetric radiance and density fields from the video frames. A high-fidelity 3D representation of the tree was generated from the reconstructed structure. Branch topology and geometric descriptors (e.g., branch order, orientation, length, and spatial distribution) were extracted for decision-making. The Monte Carlo Ray Tracing (MCRT) module was integrated to simulate ray–canopy interactions. Both direct and diffuse radiation pathways were estimated to quantify light conditions in the 3D canopy space. Two indicators were computed: light interception ratio (LIR) to characterize the proportion of incident light intercepted by the canopy, and energy interception ratio (EIR) to reflect the effective energy under the simulated radiation field. A pruning recommendation model was constructed to fuse: (i) branch-classification and geometric features, and (ii) light-efficiency weights derived from the MCRT outputs. Candidate-branch suggestions were given for the improved canopy illumination with structural feasibility. In addition, a virtual interactive pruning system was developed to support human-in-the-loop validation, intuitive visualization of light distribution, and pruning effects before field implementation. The framework was validated on a dataset of 102 Qingcuili plum trees with diverse canopies and growth. The NeRF-based reconstruction was achieved with high accuracy, with an average reconstruction error of 4.3%, indicating reliable recovery of canopy structure under complex occlusion. The pruning recommendation performance reached 93.2% accuracy, compared with expert labelling. Pruning recommendations were consistent with practical knowledge. The optimal pruning strategy was applied to substantially improve the canopy light conditions. LIR and EIR increased by approximately 15.2% and 18.9%, respectively, indicating enhanced light interception and more effective energy capture. From a deployment perspective, the end-to-end system response time remained stable in 2.3 min using cloud GPU acceleration (NVIDIA V100), indicating favorable real-time applicability for decision support and interactive analysis. A canopy structure–light-efficiency optimization was integrated with NeRF 3D reconstruction, MCRT light simulation, and feature–weight fusion for intelligent pruning. The 3D canopy reconstruction and quantitative pruning improved the light-use efficiency and the precision of tree vigor regulation in a real orchard. The finding can provide a feasible technical pathway toward digital twins and smart pruning for fruit-tree production.

Accurate perception of tomato inflorescences and flower states can often be required to support key operations in greenhouse tomato production, such as pollination and topping at the flowering and fruiting stage. However, inflorescences and flowers are characterized by small target size, dense spatial distribution, complex backgrounds, and frequent occlusion by leaves and stems in practical greenhouse environments. Single-stage detection models cannot simultaneously realize stable inflorescence localization and high-precision flower state recognition when operating on whole-plant images. In this study, a two-stage cascaded framework of visual perception was developed to detect tomato inflorescence and flower states using an improved YOLO version 11 network. A “spatial localization followed by fine-grained recognition” strategy was adopted to decompose the overall perception task into two sequential subtasks. In the first stage, an inflorescence detection model was constructed to enhance the baseline YOLO version eleven network with a Deformable Large Kernel Attention mechanism and a Dynamic Head detection structure. Deformable Large Kernel Attention Mechanism also employed large convolutional kernels with deformable convolution to capture long-range contextual information between inflorescences and adjacent peduncles, while morphological variations were also considered at different growth stages and plant structures. The dynamic head module incorporated scale- and spatial-aware feature modeling, thereby enabling the detector to robustly handle inflorescences of varying sizes for the complex regions, where inflorescences and leaves overlapped. The output precise spatial regions corresponded to inflorescences, which served as reliable regions of interest for subsequent analysis. In the second stage, a flower state recognition model was designed to operate exclusively within the inflorescence regions in the first stage. A lightweight backbone network, MobileNetV4, was adopted to reduce computational complexity for inference efficiency, while preserving feature representation. An Adaptive Task-aligned Focal Loss function was introduced to balance sample distribution among different flower developmental states. This loss function dynamically adjusted category weights, according to classification difficulty and sample frequency, thereby enhancing recognition performance for the minority and easily confused flower states under occlusion and cluttered backgrounds. Experiments were conducted on a greenhouse tomato image dataset at multiple growth stages and complex environments. In the inflorescence task, the first-stage model achieved substantial improvements in performance, compared with the baseline network, with the precision, recall, mean average precision at an intersection-over-union threshold of 0.5, and F1-score increasing by 4.02, 5.25, 8.49, and 4.66 percentage point, respectively. These results demonstrated that the attention and detection head enhancements significantly improved small-target detection stability in complex scenes. In the flower state recognition task, the second-stage model further improved precision, recall, mean average precision at the same threshold, and F1-score by 5.24, 2.97, 5.31, and 4.07 percentage point, respectively, indicating stronger identification for fine-grained flower state classification. The cascaded framework achieved an average processing speed of 38.4 frames per second under a single-input condition, fully meeting the real-time requirements of continuous greenhouse monitoring and online agricultural operations. The cascaded framework effectively balanced detection accuracy and computational efficiency to decouple spatial localization from fine-grained recognition. The reliable inflorescence and flower state recognition from whole-plant images can provide a practical visual perception for pollination, topping, and intelligent operations in greenhouse tomato production.

Sinohyriopsis cumingii is one of the economically important freshwater mussels in the pearl aquaculture industry. Phenotypic traits of S. cumingii can be expected to evaluate the individual growth performance. Germplasm resources are identified to implement precise genetic breeding. However, conventional manual measurements cannot fully meet the scalability and applicability of the large-scale production in intelligent aquaculture, due to their labor-intensive, time-consuming, and highly susceptible to subjective errors. In this study, an improved measurement was proposed for the non-destructive, rapid, and accurate acquisition of phenotypic parameters using YOLOv8n, termed YOLOv8n-CBM. 1) An integrated phenotypic measurement for S. cumingii was constructed to combine the dynamic transmission, machine vision, and digital image processing. The system comprised a conveyor device, a high-precision industrial camera, and an image processing module. The mussel samples were automatically transported into the imaging area, thus enabling standardized image acquisition and high-throughput phenotypic measurement under continuous dynamic conditions. 2) Three targeted improvements were implemented in the original YOLOv8n network architecture, according to the characteristics of mussel images. In the backbone network, four convolutional block attention modules (CBAM) were embedded after each C2f block to enhance the extraction of contour edges and local features of mussel samples, while effectively suppressing irrelevant background interference. In the neck network, the bidirectional feature pyramid network (BiFPN) was introduced to strengthen bidirectional fusion of multi-scale features for the targets of different sizes and postures. Meanwhile, the original C2f module was replaced with a multi-scale dilated attention (MSDA) module to expand the network’s receptive field for the local fine-grained and global contextual information. Finally, the key phenotypic parameters were extracted, including shell length, full height, shell height, and radial rib length of the buttock angle, according to the geometric relationship between rotated bounding boxes and biological key points. A series of experiments was conducted on a dataset of 50 manually annotated S. cumingii samples with diverse sizes and postures. The results show that the mean average precision (mAP50-95) of the YOLOv8n-CBM model reached 98.2%, indicating the rotated object detection performance over the original YOLOv8n model. The average localization deviation of biological key points was less than 2.0 mm, indicating the high precision in feature detection. The mean absolute errors (MAE) of shell length, full height, shell height, and radial rib length of the buttock angle were 1.51, 1.08, 1.019, and 1.998 mm, respectively. In all groups stratified by different shell lengths and full heights, the measurement errors of YOLOv8n-CBM were consistently lower than those of the original YOLOv8n model, with the maximum absolute error within 2.879 mm. Measurement accuracy and robustness were effectively improved with diverse morphologies and postures. In conclusion, the reliable technical approach was used to realize the rapid, accurate, and non-destructive acquisition of phenotypic traits in S. cumingii. Shellfish growth evaluation and genetic breeding can be expected to support the transition of the pearl industry from empirical farming to data-driven and intelligent aquaculture. The findings can also offer a valuable reference for phenotypic measurement in the molluscan species.

Accurate and rapid recognition is often required for the king oyster mushroom under dark and humid environments using machine vision. Particularly, the mushrooms can intertwine with mycelial networks after imaging. This study aims to accurately locate king oyster mushrooms under dark and damp environments. An identification framework of the king oyster mushroom was proposed using an infrared array. A test bench was established based on an infrared reflection module. Sensing distance and sensing space cross-sectional radius of the infrared reflection module were then measured under different electrical parameters. A numerical simulation was used to fit the relationship between electrical parameters and infrared sensing distances. A mathematical model was obtained for the infrared reflection under typical parameter conditions. The invisible infrared light was then visualized for mushroom identification, according to the sensing space model from the infrared reflection module. A detection gantry (including multiple linearly arranged infrared reflection modules) was placed over a tray. The tray moved at a constant speed. The output levels of the infrared reflection modules were sampled periodically by an STM32F103ZET6 controller. An information matrix was formed for the position and shape of the mushrooms within the tray area. The sensing information matrix was determined by the number of timed samplings and the infrared reflection modules. Simple matrix operations were performed on the single-connected regions with zero values in the sensing information matrix. According to the morphologies of the king oyster mushroom, a double-cylinder rotary harvester was designed to calculate the position coordinates of the mushroom's center point relative to the tray. A servo motor drove a reducer, thus causing two coaxially arranged hollow cylinders to rotate relative to each other. Six blades evenly distributed below the cylinders were used to cut the king oyster mushroom in a planar motion. The harvester was fixed to a vertically moving linear module. The motion axes were arranged on an execution gantry, thereby driving the horizontal and vertical movements of the harvester. A king oyster mushroom harvesting device was developed, where the detection and execution gantries were sequentially placed on the tray. Taking king oyster mushrooms as the targets, five symmetrically distributed feature positions on the tray were selected for the identification and harvesting experiments. A series of experiments was conducted to verify the device. The time to solve for the mushroom center point coordinates was 3.5-4.5 ms using the information matrix, which was significantly less than the 40-50 ms required for mushroom image processing using machine vision. The high efficiency of the identification was obtained using the infrared array. Meanwhile, the deviation between the inner hole of the harvester and the mushroom cap center was controlled within 1.5-4.0 mm after the harvester was positioned on the mushroom. This gap met the operational quality requirements of mushroom harvesting robots. The harvesting success rate reached 100%, indicating the high positioning accuracy of the motion axis. The king oyster mushroom identification was developed using typical infrared reflection modules, whose cost was only 1/20 of the mainstream machine vision products. The outstanding efficiency and economic advantages were achieved in the strongly interfering substrate and mycelial networks. The findings can also provide a technical pathway for the edible king oyster mushroom harvesting under complex backgrounds.

Satellite remote sensing can identify the irrigation information, because of its rapid and wide-area observation. However, only a single source is often extracted from remote sensing data. Spatial and temporal resolution cannot fully meet the requirement for the high-accuracy and dynamic identification of irrigation information at the regional scale, especially for the strong spatial heterogeneity of agricultural activities in the complex terrain. In this study, a remote sensing framework was developed to identify the irrigation information using drought index analysis and spatiotemporal fusion. The Guanzhong Region was also taken as the study area. The temperature vegetation dryness index (TVDI) was selected as the identification index after correlation analysis between drought indices and soil moisture. Elevation correction with fusion optimization was introduced to characterize the variation in the soil moisture. Its spatiotemporal fusion accuracy was also enhanced under complex terrain conditions. Subsequently, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was used to fuse high-spatial-resolution Landsat imagery and high-temporal-resolution MODIS data for the high-spatiotemporal-resolution TVDI time series. Spring irrigation information in the Guanzhong Region in 2024 was identified using the threshold method with precipitation data. The results showed that the correlations between the remote sensing drought indices and soil moisture at the 10-20 cm depth were generally higher than those with soil moisture at the 0-10 cm depth. Elevation topographic correction effectively reduced the influence of terrain on land surface temperature. There was a strong correlation between TVDI and soil moisture. Furthermore, the elevation-corrected TVDI showed a strong negative correlation with soil moisture at the 10-20 cm depth, with the maximum correlation coefficient of −0.77 during the spring crop growth period. Normalized difference vegetation index (NDVI) and land surface temperature (LST) were fused for the higher accuracy of TVDI than the strategy of first calculation and then fusion. R² and RMSE values of 0.76 and 0.07 for the former, whereas 0.44 and 0.13 for the latter, respectively. The validation showed that the overall accuracy was 90.8% for the identification in the Donglei Phase II irrigation district, with a Kappa coefficient of 0.80. The mean error was 15.1% and 14.3%, respectively, for accumulated and actual irrigated areas in the irrigation districts. Regional identification results indicated that the spring irrigation was mainly concentrated from March to April, with the irrigation frequency ranging from one to two times. Irrigated areas were distributed in the relatively flat Weihe Plain, with a spatial pattern characterized by broader irrigation extent and higher irrigation frequency in the eastern and western parts. While the central part exhibited relatively lower irrigation intensity. The spatial distribution and irrigation frequency of spring irrigation were dominated by regional topography, water supply, and cropping structure. The finding can provide a strong reference to identify the regional-scale irrigation information and water resources under complex terrains.

Farmland shelterbelts can be represented as typical narrow linear features in remote sensing imagery. However, the significant challenges remain in accurate segmentation, due to their strong global contextual dependencies and weak local features. In this study, a semantic segmentation model, named MFF-Net, was proposed to accurately and rapidly extract cultivated land and shelterbelts. Multi-feature fusion block (MFFB) and spatially gated fusion mechanism (SGFM) were also adaptively integrated with three complementary approaches: the long-range dependency modeling using a mamba-like linear attention (MLLA) operator, the local detail perception in convolutional networks, and the frequency-domain edge enhancement via fast Fourier transform (FFT). The feature representation was effectively balanced when segmenting elongated objects. Furthermore, a task- oriented super-resolution preprocessing was introduced into the network. This front-end step was designed to reconstruct high-resolution images. Thereby, the textual details were enhanced to sharpen the boundaries of subtle features, like shelterbelts. Superior input was provided for the subsequent segmentation model. A series of experiments was performed on the self-constructed dataset of the farmland shelterbelt. The results demonstrate that the superior performance of MFF-Net was achieved in the precision rates of 96.42% for cropland and 82.83% for shelterbelts, with a mean intersection over union (mIoU) of 83.45%, thus outperforming a range of advanced models, including RS³Mamba, DC-Swin, DeepLabV3+, and SegMAN. Ablation studies validated the effectiveness of each component within the multi-feature fusion. Frequency-domain features via FFTFormer contributed to a 2.80% increase in the shelterbelt IoU, indicating the enhanced boundary discrimination. The spatially gated fusion mechanism (SGFM) further boosted the overall mIoU by 1.71%, indicating the adaptive balance on the contribution rates from different feature domains. Compared with a baseline model, the full MFFB was improved mIoU of 5.14%. Super-resolution preprocessing was integrated with the highly effective auxiliary strategy. Taking 4x upsampled images as the input, there was a remarkable 6.61% increase in shelterbelt IoU and a 2.47% gain in overall mIoU. The difficulties with segmenting narrow targets were effectively mitigated to augment their pixel-width and edge clarity. A complete technical pipeline was established from pixel-level segmentation to vectorization and application. Accurate area estimation was achieved with average relative errors of 7.50% for cropland and 6.76% for shelterbelts, compared with the national survey data. An application analysis of shelterbelt closure degree was also made on individual farmland plots. The 91.95% of the plots met the national standard requirement (≥0.75). In conclusion, the MFF-Net model can be expected to effectively segment the narrow linear features in complex landscapes. Synergistic fusion of global, local, and frequency-domain features was combined with task-oriented super-resolution enhancement. This research can provide a robust technical pathway for the precise monitoring and dynamic evaluation of farmland shelterbelt networks in ecological conservation.

Kitchen waste (KW) composting often suffers from prolonged processing time and strong odor emissions due to the high moisture content and complex organic composition of the substrate. This study aimed to elucidate how inoculation with an immobilized bacterial consortium (IBC) regulates the microbial community, co-occurrence network structure, and metabolic functions in a KW composting system, thereby improving composting efficiency and mitigating odor generation. A composting system inoculated with an IBC composed of six functional bacterial strains was established, with a non-inoculated treatment serving as control. The physicochemical parameters of the compost, including temperature, moisture content, pH, and germination index (GI), were continuously monitored throughout the 15-day process. Bacterial community composition and succession were analyzed via 16S rRNA gene sequencing. Co-occurrence networks were constructed for different composting phases to reveal changes in microbial interactions. Functional Annotation of Prokaryotic Taxa (FAPROTAX) was applied to predict metabolic pathways related to carbon, nitrogen, and sulfur cycling. Partial Least Squares Path Modeling (PLS-PM) was used to explore causal relationships among physicochemical conditions, microbial community structure, network complexity, metabolic functions, and composting efficiency. The IBC treatment sustained a longer and more stable thermophilic phase than the control, accelerating compost maturity, with the GI reaching 88.89% on day 15 compared to 58.89% in the control. Inoculation significantly reshaped the bacterial community structure and enhanced deterministic assembly processes, guiding microbial succession toward functional guilds specialized in organic degradation and nutrient transformation. The inoculated compost exhibited greater network complexity, characterized by increased node and edge numbers, higher average degree, and reduced path length and network diameter, indicating stronger microbial connectivity and synergistic metabolic cooperation. Functional prediction showed that carbon cycling was dominated by chemoheterotrophy and aerobic chemoheterotrophy, both increasing over time, while fermentation functions gradually declined. In the nitrogen cycle, nitrite respiration and dissimilatory ammonification were most active during the early phase, but nitrogen fixation became dominant in the later cooling and maturation stages. Sulfur respiration pathways were markedly suppressed in the inoculated group, implying the inhibition of reductive sulfur metabolism and reduced potential for odor emission. PLS-PM analysis further demonstrated that microbial inoculation reversed the relationship between physicochemical properties and bacterial community from negative to positive, promoting the enrichment of core functional taxa. The relationship between community structure and metabolic function shifted from diversity-driven to functional taxa-driven patterns. Although the direct effect of network complexity on composting efficiency declined, it indirectly enhanced system functionality through improved robustness and cooperative stability. The immobilized bacterial consortium effectively optimized the composting physicochemical environment, reconstructed microbial interaction networks, and reinforced functional coupling among key taxa. These integrated effects accelerated organic matter degradation, shortened the composting period, and reduced odor emissions. The study provides new ecological insights into the microbial regulatory mechanisms of KW composting and supports the development of efficient, low-emission, and sustainable biotechnological strategies for organic waste recycling.

Agricultural carbon emissions have been generated by human activities in vast regions. It is often required to accurately understand the status, spatiotemporal patterns, and future trends of agricultural carbon emissions. It is also crucial to optimize carbon sequestration and emission reduction against climate adaptation. However, current assessments can rely heavily on statistical data, where regions with incomplete statistical records can introduce great uncertainties in carbon accounting and forecasting. Taking Chongqing as a case study, a systematic investigation was conducted to explore the spatiotemporal patterns and future trends of agricultural carbon emissions from 2004 to 2023. Multi-source agricultural data was also combined with statistics and remote sensing monitoring at the county level. Furthermore, spatiotemporal analysis was employed to examine the evolution, including slope estimation, the Mann-Kendall test, Moran's I index, and the Getis-Ord Gi* index. While the prediction models were then constructed for the trends, such as ARIMA and three machine learning methods (support vector machine, random forest, and XGBoost). The results indicate that: 1) The feasible and reliable performance was achieved to evaluate agricultural carbon emission using multi-source data, particularly with the average annual agricultural carbon emission of 2.435 million tons. There was a significant correlation with the conventional statistical data (R²=0.932, P<0.001), thus compensating for missing county-level statistical data. The higher stability was also achieved after evaluation. 2) There were significant source and regional differences in agricultural carbon emissions. The primary sources were methane emissions from rice cultivation and carbon emissions from fertilizer use, with average annual emissions of 1.175 million and 0.809 million tons, respectively. The spatial agglomeration of agricultural carbon emissions was intensified year by year, with the global Moran's I index of 0.695, 0.615, and 0.64 in 2017, 2021, and 2023, respectively. Specifically, Wanzhou, Liangping, and Zhongxian were identified as emission hotspots, with average annual agricultural carbon emissions of 0.106 million, 0.105 million, and 0.089 million tons, respectively; Whereas Nan'an, Jiulongpo, and Beibei were identified as emission cold spots, with average annual emissions of 6.996 thousand, 15.694 thousand, and 29.679 thousand tons, respectively. 3) The interpretable ARIMA-XGBoost prediction model performed well on an independent test set (R²=0.936). The agricultural carbon emissions were shifted from a generally stable state to a more widespread downward trend. Total emissions were projected to gradually decrease from 2.187 million to 1.788 million tons between 2024 and 2030. More significant influencing factors were determined as the rural employees, highway mileage, and gross product in agricultural carbon emissions. Yet there was no variation in the spatially differentiated distribution over counties. Multi-source data can offer information complementarity and reliability to assess regional agricultural carbon emissions. The findings can provide a scientific foundation for low-carbon sequestration and emission reduction. A valuable reference can also serve as the low carbon strategies in similar regions.

Strong agricultural towns have promoted the circulation of resources among towns in modern rural China. Local resources can be integrated to develop comparatively advantaged leading industries in sustainable agriculture. This study aims to clarify the spatial distribution and influencing factors of strong agricultural towns with various categories at the national level from 2018 to 2024. Nine categories were classified according to the leading industries. A combination of spatial analysis, including the average nearest neighbor index, kernel density estimation, and geographical detector, was adopted to explore the spatial pattern and driving factors. The results show that: (1) A total of 1 709 strong agricultural towns were approved in China, which were distributed in the third topographic step along water sources. The overall uneven spatial distribution exhibited a “northeast–southwest” pattern. Kernel density analysis revealed that the towns specializing in different product categories exhibited the distribution patterns of "category-region matching and core-led radiation". Among them, the largest number of grain and oil industry-strong towns reached 389, while edible fungus industry-strong towns were the smallest, with only 60. Overall, three major high-density clusters were formed in the border areas of Hebei-Shandong-Henan, Jiangsu-Zhejiang, and Sichuan-Chongqing. (2) At the provincial level, the conventional major agricultural provinces—Shandong, Sichuan, and Henan—shared a large number of such towns, indicating the sound quantity and spatial layout. According to the average nearest neighbor index and distribution density, 17 provinces shared the high dense distribution. Specifically, the dense agglomeration was found in the six regions (Shandong, Henan, Guangdong, Jiangsu, Hubei, and Chongqing); Beijing, Tianjin, and Shanghai were the dense uniformity; Three autonomous regions (Xinjiang, Inner Mongolia, and Tibet) presented the scattered agglomeration. (3) The geographical detector showed that the agricultural production scale and regional economic development level served as the significant single factors to explain the distribution of strong industry towns. Among them, the total output value of agriculture, forestry, animal husbandry, and fishery also presented the strongest explanatory effect, particularly for the agricultural development level and optimal industrial structure. In contrast, regional and policy factors shared the weak independent explanatory effects, with the stronger explanatory power after interactions with the other factors. In conclusion, the strong agricultural towns can be expected to position product categories, according to local resources, differentiated development, and comparative advantages. Planning and layout can promote the clustered development of factor efficiency in strong agricultural towns, leading to their differentiated, intensive, and high-quality development. The findings can also provide data support to construct strong industry towns.

Suburban rural areas of metropolitan regions can serve as the interface between urban and rural elements. Their rural settlements have posed challenges in recent years, such as idle and inefficient land use, scattered spatial layouts, and supply-demand mismatches in functions. Rural settlement consolidation can be expected to increasingly emphasize rural revitalization and territory-wide land consolidation. An accurate release of consolidation potential is often required for the precise alignment between land supply and development demand. Therefore, this study aims to assess the potential of rural settlements for the differentiated consolidation pathways in rural stock resources. A case study was taken of Jurong City, a suburb of the Nanjing metropolitan area. A "supply-demand assessment-type identification” framework was constructed using supply-demand theory. Supply–demand consolidation potential of rural settlements was also assessed using a one-class support vector machine (SVM) and machine learning. Furthermore, the precise pathways of land resource allocation were also explored at the village scale, according to the rural dominant function demands. The results show: 1) The theoretical consolidation potential of rural settlements was 8 490.19 hm², accounting for 61.51% of the settlement area. The willingness simulation model was validated with an accuracy of 87.81% and a recall rate of 95.48%, indicating high precision. The modeled willingness values ranged from 0.33 to 0.76 for administrative villages. Specifically, the villages with higher consolidation willingness were distributed in the southeastern region, including the Maoshan Scenic Area, Maoshan, Houbai, and Tianwang Town. The actual consolidation potential of rural settlements was 5441.91 hm² after correction, representing 39.42% of the rural settlement land. 2) The demand potential of rural revitalization ranged from 0.20 to 0.60, indicating a pattern of “higher in the west, lower in the east, with clustered distribution.” The number of medium-demand villages was the largest among all villages, totaling 100 (53% of all villages). 3) Four types of zones were identified for the supply-demand potential: priority consolidation, reserve regulation, demand-oriented, and stable control zone, accounting for 43, 35, 73, and 37 villages, respectively. 4) Four types of dominant functional demands were identified among the rural settlements, with the majority of balanced development villages. Most villages shared no dominant functional demand, resulting in a balanced functional profile with scattered spatial distribution. Potential zones were coupled with dominant functional demands. 12 consolidation types were derived at the village scale. Potential release pathways were summarized, such as “spatial reconstruction,” “precise guidance,” “flexible regulation,” and “micro-renewal,” thus enabling “one village, one strategy” precise intervention. Targeted consolidation measures were also proposed, such as “releasing potential through village reorganization” and “innovating dynamic land reservation supply.” Differentiated strategies were implemented to promote intensive and economical land use for the rural functions and spatial patterns. The findings can also provide a practical demonstration and reference to precisely match land supply with rural revitalization demands in the spatial units of metropolitan suburbs.

Ecosystem services supply and demand can coordinate the socioeconomic and environmental systems for the regional ecological security. Their spatiotemporal dynamics can be used to identify the complex interactions among services under anthropogenic disturbance. In this study, six ecosystem services were assessed in Chongqing from 2005 to 2023. The InVEST model and Spearman correlation analysis were employed to quantify carbon sequestration, grain production, soil conservation, water yield, habitat quality, as well as recreation and leisure. Furthermore, a systematic analysis was also conducted on the spatiotemporal dynamics of their supply-demand relationships and trade-offs/synergies over urbanization zoning. The results indicate that: (1) Although Chongqing maintained a surplus of ecosystem services, the margin of the surplus consistently narrowed over the study period. The supply-demand ratio was slight but persistently declined. High supply-demand ratios for carbon sequestration, water yield, soil conservation, habitat quality, as well as recreation and leisure were concentrated in the northeastern and southeastern areas of Chongqing, whereas the low ratios were clustered in the key districts of the main metropolitan area. High values of grain production were distributed in the peripheral counties of the metropolitan region, with deficits in the urban, the northeastern, and southeastern parts of Chongqing. (2) Urbanization exhibited a monocentric-polycentric structure with the west-east gradient, which was characterized by urban expansion and concomitant rural contraction. There was an outstanding spatial gradient in the supply and demand of ecosystem services. Among them, the demand increased progressively from rural to urban areas, while the supply exhibited the inverse pattern. The rural areas were positioned as primary provisioning zones, while the urban as persistent deficit zones. Supply-demand ratios declined for most services; Urban expansion zones transitioned from a balance to a deficit, with the notable exceptions of soil conservation and water yield, indicating the increased surpluses. (3) Static relationships among ecosystem services in rural areas were dominated by strong, stable trade-offs or synergies. In urban-rural transition zones, the weak synergies prevailed and fluctuated significantly, whereas urban expansion zones exhibited a trade-off between synergy dominance. Only five service pairs exhibited consistent trade-off or synergy relationships with the minimal temporal variation in urban areas. Habitat quality was persistently synergistic with recreation and leisure over all urbanization zones. While water yield consistently traded off with recreation and leisure. Most dynamic relationships among services were not statistically significant in rural and urban areas. There was a pronounced differentiation in the urban-rural transition zones, whereas the trade-offs and synergies were in urban expansion zones; Soil conservation and water yield remained synergistic over the period. The strength of the synergy increased in urbanization intensity, whereas habitat quality and water yield remained persistently in trade-off. (4) Differentiated strategies were proposed to realize the divergent patterns of ecosystem services supply-demand and their interactions in the urbanization zones. In urban areas, ecological potential should prioritize enhancing the ecological network connectivity. Urban expansion and urban-rural transition zones are required for the enforcement of ecological redlines, particularly with context-specific natural restoration and topographic constraints. In rural areas, ecotourism and carbon sink markets can be coupled with the urban-rural ecological compensation. Ecological assets can also be translated into tangible values. Differentiation of urbanization zones in the mountainous metropolitan can be used to examine the spatial heterogeneity of ecosystem service dynamics along the urban-rural gradient. This finding can provide a robust scientific foundation to inform urban spatial and sustainable ecosystems in Chongqing.

Microplastics can enter agro-food systems via multiple pathways, including agricultural production, environmental transport, post-harvest handling, processing, packaging, and distribution. Major sources can be attributed to the residue, weathering, fragmentation, and secondary breakdown of agricultural plastics, such as mulching films, greenhouse covers, irrigation tapes, pipes, nets, and food-contact materials. Particles can be redistributed through irrigation water, surface runoff, soil dust resuspension, atmospheric deposition, and the agricultural application of compost or sewage sludge. Additional contamination can also cause from abrasion, shedding, and migration of processing equipment, filtration media, packaging materials, and storage interfaces. Together, these pathways can pose a great challenge to the multi-source, multi-stage, and cross-media exposure pattern, leading to difficult-to-source identification, risk interpretation, and food safety. This review aims to focus on microplastic monitoring, assessment, and process control in the agro-food chain. Major source categories and contamination pathways were summarized in crop production, animal production, and processing environments, and then examined the reported occurrence in plant-derived foods, animal-derived foods, and processed products. Available studies showed that microplastics were widely detected in vegetables, fruits, cereals, shellfish, fish, meat products, bottled water, salt, sugar, honey, beer, and liquid milk. But reported concentrations varied substantially over studies, leading to orders of magnitude. Such heterogeneity was strongly influenced by commodity differences and regional context, method factors, including particle-size thresholds, reporting metrics with particle number or mass, pretreatment intensity, recovery correction, blank control, confirmation criteria, and sampling contexts, such as washing, peeling, retail cutting, cooking, and packaging. Direct comparison was limited for the quantitative interpretation, especially when trophic transfer coexisted with process-related contamination. Therefore, current occurrence data were more useful to identify high-concern commodities, high-concern links, and major uncertainty sources, compared with the cross-study ranking under heterogeneous analytical conditions. Analytical challenges were further highlighted under complex agro-food matrices rich in lipids, proteins, polysaccharides, pigments, and inorganic particulates, where microplastics often occurred at trace levels over broad size ranges. Practical bottlenecks were closely related to matrix removal, polymer preservation, particle loss control, and procedural contamination prevention. Pretreatment strategies, including chemical digestion, enzymatic digestion, density separation, and membrane filtration, were compared from the perspective of matrix applicability, polymer compatibility, recovery performance, and quality-control requirements. Chemical digestion provided effective organic matter removal, but caused damage to polymers under strong acidic or oxidative conditions. Enzymatic digestion offered milder treatment and better polymer preservation but remained constrained by cost, duration, and reagent background. Density separation was used for enrichment efficiency, but it was required for the selection of separation media and recovery validation, especially for small particles and high-density polymers. Membrane filtration functioned as a concentration step as a critical interface affecting optical imaging, verification, and background interference. Verification workflows were reviewed over microscopy and fluorescence imaging, vibrational spectroscopy, thermal analysis, and emerging high-throughput platforms. Among them, microscopy and fluorescence staining supported rapid morphological screening, but visual identification alone remained vulnerable to false positives and operator subjectivity. Micro-FTIR and micro-Raman spectroscopy provided for chemical identification central to polymer at the particle level, yet they were constrained by throughput, diffraction limits, fluorescence interference, and spectral-library dependence. Thermal evaluations, such as Py-GC/MS and TED-GC/MS, were mass-based quantification of visually undetectable particles, but their performance in food matrices remained sensitive to marker interference, calibration strategy, and background decomposition products. Emerging techniques, including laser direct infrared imaging, optical photothermal infrared spectroscopy, atomic force microscopy infrared spectroscopy, hyperspectral imaging, surface-enhanced Raman scattering, and portable sensing systems, were expected to improve throughput, particle-size coverage, and field-oriented monitoring, although their broader application still depended on scenario adaptation and quality. Artificial intelligence was reviewed as an auxiliary tool for high-throughput screening, spectral interpretation, and multimodal data integration. Image-based models were used to improve particle localization, counting, and morphological characterization in fluorescence and microscopic datasets. While machine learning and deep-learning were supported spectral denoising, feature extraction, data matching, and multimodal fusion over imaging, FTIR, and Raman data. Their practical value depended on transparent training datasets, reproducible preprocessing pipelines, external validation over matrices and devices, drift monitoring, reject-option strategies, and alignment with conventional analytical indicators, such as recovery, blank correction, repeatability, detection limits, and particle-size-specific performance. As such, artificial intelligence was better supported by standardized monitoring and data interpretation rather than a substitute for chemical verification. Furthermore, a flux accounting perspective was introduced to link endpoint measurements with process control. Input and output fluxes of the individual unit were defined to quantify the removal efficiency and net introduction. This framework was used to identify critical control points and then evaluate mitigation options. Source reduction, process interception, and terminal were selected as complementary controls. Priority measures included the life cycle of agricultural plastics, fragmentation-prone inputs, water purification, control of sludge and compost application, high-friction processing interfaces, and standardized sampling, pretreatment, reporting, and quality-control requirements. Safety thresholds were required for microplastics in agro-food products, covering precautionary and prioritizing comparable data generation, critical control points, and controllable exposure reduction over the full chain. Overall, the microplastics in agro-food systems can be expected to move from isolated endpoints toward integrated monitoring, process evaluation, and process-oriented detection.

Maize is susceptible to rapid quality deterioration and fungal infection due to complex environmental fluctuations during "North-to-South Grain Transfer" strategies. However, conventional detection is often time-consuming, destructive, and labor-intensive under different storage and transportation environments. It is an urgent need to non-destructively and rapidly identify the mold ratio, and then continuously predict the quality index. In this study, a synchronous prediction was proposed for maize mold ratio and storage/transportation quality using advanced image processing and deep learning technologies. Maize samples with a controlled gradient mold ratio ranging from 0 to 12% were selected as the research objects. A systematic simulation was conducted on typical temperature and humidity environments of both waterway and overland transportation routes. Key quality indices of the maize were measured to quantify the deterioration rates in the simulated storage and transportation periods, including moisture content, fatty acid value, and electrical conductivity. Simultaneously, maize images were collected using a standard smartphone. Digital image processing was also integrated with the Vision Transformer (ViT) deep learning model and statistical modeling. The mold ratio was then detected to precisely predict the quality indices. The results indicated that the high-humidity environment of waterway transportation accelerated the deterioration of maize kernel quality (P<0.05). Specifically, the moisture content of the waterway samples rapidly exceeded the threshold of 14% in the national safe storage standard when the mold ratio reached 2%. In contrast, the moisture content of the overland transportation samples remained stable in the safe range of 12.207% to 12.772%. Furthermore, the fatty acid value and electrical conductivity increased by 57.070% and 38.357%, respectively, under waterway conditions, as the maize mold ratio increased progressively. These deterioration rates were higher than those under overland conditions, indicating the lower increases of 29.035% and 27.714%, respectively. In terms of the deep learning algorithms, the ViT architecture achieved exceptionally high precision in identifying moldy maize kernels, reaching an impressive overall accuracy of 99.00%. Subsequently, a Mean Absolute Error (MAE) of only 0.52% was achieved, indicating the accurate and reliable prediction of the overall maize mold ratio. Visual features were extracted and further screened to construct Multiple Linear Regression (MLR) models for quality evaluation. In the waterway samples, the coefficients of determination (R²) of the prediction models reached 0.859, 0.955, and 0.942, respectively, for moisture content, fatty acid value, and electrical conductivity. In the overland samples, the R² values of prediction models were 0.930 and 0.937, respectively, for the fatty acid value and electrical conductivity, indicating accurate prediction for the quality of maize during storage and transportation. In conclusion, the dynamic quality deterioration of moldy maize can provide a low-cost, easy-to-operate, and entirely non-destructive pathway for maize quality detection. This finding can also offer an effective and practical analytical tool to dynamically monitor quality and safety for risk early warning during the complex grain circulation.

Conventional processing cannot fully modulate multiple biological activities of bioactive Polygonatum sibiricum polysaccharides using structural modifications and complex conformational transformations. The present study aims to clarify the different effects of steaming and fermentation—two representative processing techniques, including dynamic structural evolution of the medicinally valuable polysaccharides and the subsequent precise regulation of their bioactivities. Polysaccharides were extracted and then purified from fresh Polygonatum sibiricum, conventionally three-times-steamed Polygonatum sibiricum, and steamed-then-fermented Polygonatum sibiricum. The polysaccharide samples were designated as FPP, PPP, and FMP, respectively. An analytical platform was also employed: high-performance gel permeation chromatography (HPGPC) was used to determine molecular weight distributions; high-performance liquid chromatography (HPLC) with pre-column derivatization was used to quantify monosaccharide composition; Fourier transform infrared spectroscopy (FT-IR) was used to identify characteristic functional groups and conformational transitions. Zeta potential and dynamic light scattering analysis were used to assess colloidal stability and particle size uniformity, while scanning electron microscopy (SEM) and atomic force microscopy (AFM) were used to visualize morphological and nanostructure transformations, respectively. Antioxidant and hypoglycemic activities were evaluated through in vitro assays. Specifically, the 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacities were determined with α-amylase and α-glucosidase inhibitory kinetics. Steaming was used to alter the structural integrity of the polysaccharides, disrupt their native triple-helix conformation, modify the monosaccharide profile, shift molecular weight distribution toward higher ranges, and dramatically increase uronic acid content. Subsequently, fermentation acted as the precise biological modification, further fine-tuning monosaccharide compositional ratios using microbial enzymatic hydrolysis. Biotransformation raised the absolute Zeta potential, electrostatic repulsion and colloidal stability, and particle size distribution, indicating the remarkable homogeneity. These conformations were visually captured by microstructural observations. In SEM imagery, morphological transitions were observed: FPP displayed a smooth, continuous sheet-like film; Steaming induced regularly arranged, protrusive structures; Fermentation generated a porous network morphology, thereby increasing structural porosity and specific surface area. According to these morphological shifts in AFM images, nanoscale transformations occurred from flexible, worm-like chains of FPP to compact, spherical chains in both PPP and FMP. The average chain height increased markedly after steaming and then decreased after fermentation. Functionally, these structural modifications were closely correlated with enhanced bioactivities. In antioxidant evaluations, PPP exhibited the most potent DPPH radical scavenging capacity. Meanwhile, FMP presented robust ABTS radical scavenging activity compared with the PPP. In hypoglycemic potential, both PPP and FMP were more effectively inhibited α-amylase than native FPP. Crucially, FMP shared the optimal inhibitory effect against α-glucosidase among all tested samples. Steaming and fermentation served as effective strategies to modulate the chemical structure, spatial conformation, and microscopic morphology of Polygonatum sibiricum polysaccharides, thereby enhancing their antioxidant and hypoglycemic activities. The processing-induced structural modifications showed a strong correlation between specific structural features and physiological functions in plant polysaccharides. Consequently, this finding can also provide a solid theoretical foundation and practical guidance for the precise, high-value industrial application of Polygonatum sibiricum resources in the functional food using traditional Chinese medicine processing.

Xanthoceras sorbifolium Bunge, belonging to the Sapindaceae family, has been one of the most potential promising woody oil species in northern China. X. sorbifolium can play the important ecological roles, including desert greening, windbreak, and sand fixation, also providing for the edible and medicinal value among plant resources. Furthermore, X. sorbifolia can be used as nature food for health protection and disease prophylaxis in recent years, such as for tea, due to its high concentration of unsaturated fatty acids, especially neuroprotective nervonic acid. The leaves and buds of X. sorbifolium also share the nourishing ingredients and bioactive substances, including amino acids, proteins, soluble sugars, polyphenols, flavonoids, and saponins. In this study, a full-factor design was adopted with five levels of steeping temperature and seven levels of steeping time. Nutritional quality of the tea infusions was evaluated under 35 combinations. A systematic investigation was conducted to fully clarify the influence of brewing conditions on the quality of the tea infusions with the Xanthoceras sorbifolium bud green tea (XBT). Fuzzy A fuzzy membership function was used to screen the optimal brewing parameters. The aroma and taste of the tea infusions with XBT were characterized by gas chromatography-ion mobility spectrometry, electronic nose, and electronic tongue. The results showed that the content of tea polyphenols in the tea infusions of XBT first increased, then decreased, and finally tended to be stable, with the extension of steeping time. While the contents of total free amino acids, flavonoids, and caffeine generally showed a trend of first increasing and then decreasing. At the same time, a higher brewing temperature was conducive to the dissolution of soluble sugar components. Fuzzy The fuzzy membership function also showed that the long-term brewing (360, 720 min) was not conducive to the overall quality of the tea infusions, while short brewing time (5 min) together with low temperature (50, 60 ℃) failed to fully dissolve the nutrients in the tea infusions of XBT. Three optimal combinations of steeping parameters were obtained: 70 ℃ for 60 min, 90 ℃ for 30 min, and 80 ℃ for 30 min. A total of 60 volatile organic compounds and 13 key aroma substances were detected in the tea infusions. 2-methylbutyraldehyde and 3-methylbutyraldehyde jointly contributed to the roast aroma of tea infusions, while both octanal and hexanal contributed to the fresh fruit aroma of the tea infusions, and pentanal contributed to the grassy aroma of the tea infusions. The brewing conditions of 70 ℃ for 60 min and 80 ℃ for 30 min were more benefits beneficial to the retention of aroma substances in the tea infusions, thereby reducing the bitterness and astringency. According to the nutritional quality, taste, and aroma, the optimal combination of 80 ℃ for 30 min was recommended as the daily drinking brewing for XBT. The finding can also provide a theoretical basis for the subsequent processing of tea beverages. In short, the bud can be expected to serve as a tea products for the high economic value of Xanthoceras sorbifolium.

Due to the poor mass transfer, long reaction time, and severe material back mixing in conventional stirring reactions, the microfluidic reaction system, with the advantages of enhanced mass transfer, fast reaction speed, and mitigated substrate inhibition, has received attention. Natural wood is a cheap, renewable, and earth-abundant material, which is regarded as the ideal model for monolithic reactors due to the existing 3D hierarchical structures. Carbonized wood with superior electrical conductivity, chemical and mechanical stability, and tunable multifunctionality endows it as a monolithic reactor object to synthesize advanced materials for multiple purposes. This study constructed a carbonized monolithic microreactor for the continuous-flow catalytic synthesis of ethyl cinnamate, using the basswood column with a natural three-dimensional microchannel structure, which was carbonized in a nitrogen atmosphere at 700 ℃. The peristaltic pump tube is used to connect the metal coil and the carbonized monolithic microreactor in turn. The peristaltic pump sends the reaction liquid to the metal coil, and the oil bath pan heats the metal coil to preheat the reaction liquid. Subsequently, the reaction liquid enters the carbonized monolithic microreactor, and the oil bath circulation device heats the reactor to ensure the reaction temperature. The results indicate that the length and diameter of carbonized-wood columns were reduced from 200 and 40 mm to 165 and 29.6 mm, respectively, due to the pyrolysis of lignin, hemicellulose, and cellulose at elevated temperature. The resulting material not only preserves the well-aligned microchannel topology of the original wood, but also exhibits significantly enhanced properties, including high chemical stability, robust mechanical strength, and exceptional mass and heat transfer performance—laying a solid foundation for efficient continuous-flow catalytic processes. SEM characterization demonstrated the regular and hierarchical porous structures of carbonized column with abundant tubular channels (5-50 µm in diameter) in the wood growth direction and micro-sized pores (0.5-1 µm) inside tubular channels. The micro-sized pores on the tubular channels allowed the liquid substrates to enter the adjacent channels and generate fluid disturbance for improved mass transfer and enhanced catalytic capacity. Then, 96.5% of cinnamic acid conversion was reached with the molar ratio of cinnamic acid to ethanol at 1:20, the catalyst addition of concentrated H₂SO₄ (98 wt%) being 30 % of the mass of cinnamic acid, the reaction temperature of 100 ℃, substrate flow rate of 5 mL/min and the outlet pressure at 0.2 MPa. Under the continuous-flow reaction mode, a carbonized-wood monolithic microreactor induced a maximum TOF of 42.4 h⁻¹ for the catalyst of sulfuric acid, which was 11.7-22.3 times higher than that in batch-mode reaction. This carbonized monolithic microreactor exhibited excellent mechanical strength (4 538 N in load, 31.2 MPa in compressive strength, 3839 MPa in elastic modulus) and acid-base tolerance, which could maintain over 90% of cinnamic acid conversion after 10 consecutive runs. The microchannel reactor was subjected to immersion tests in both acidic and alkaline solutions of varying concentrations for 24 hours. After drying, its structural morphology remained fully intact, demonstrating exceptional resistance to corrosive chemical environments. These properties ensure long-term chemical stability under continuous operation, structural integrity against collapse or deformation caused by reactive fluid flow under process conditions. Besides, it can also be used for the efficient preparation of various flavor esters, such as ethyl acetate (93.5%), hexyl hexanoate (95.7%), iso-amyl p-methoxycinnamate (87.6%), ethyl hexanoate (78.9%), ethyl butyrate (92.0%), and cinnamic acid methylester (92.4%). Hence, the research developed a carbonized-wood monolithic microreactor with basswood as raw material, which was filled into a metal casing after elevated temperature carbonization. The reactor exhibited high mass and heat transfer efficiency, presented good mechanical properties, and acid and alkali resistance. The finding can provide a potential strategy for the efficient synthesis of flavor esters by combining continuous flow reaction and acid catalysis, in industrial applications in the field of food and cosmetics.

Accurately adjusting the feeding intake is often required in pond aquaculture of Micropterus salmoides. In this study, a dual-model progressive feeding strategy was proposed to accurately forecast the postprandial feeding status of the subsequent round using a time series model. The decrement mechanism was first triggered. Subsequently, a classification model was utilized to determine the postprandial feeding status of the current round, enabling the termination of the decrement mechanism. The experimental system consisted of three pond culture tanks stocked with Micropterus salmoides, with initial body masses of (161.47±11.02) , (204.36±17.09) , and (220.25±22.78) g, and the stocking densities of 224, 301, and 294 individuals, respectively. Feeding experiments were conducted using a multi-round feeding protocol within a single session. Audio data was collected during feeding using the digital hydrophone, while video data was obtained using a camera. Meanwhile, multimodal data including light intensity, water temperature, body mass, per-round feeding rate, and population abundance were synchronously recorded for per-round feeding. The stabilization of the cumulative feeding energy curve was adopted to determine feeding termination. A dynamic adaptive threshold segmentation was employed to accurately identify the feeding audio within the collected audio. Principal component analysis was conducted on the feeding features to extract from the feeding audio. Feature selection was employed to identify sensitive features to feeding status, which served as the inputs for model training and classification. Four models 1D convolutional neural network, Long short-term memory, gated recurrent unit, and transformer were improved to construct the feeding prediction models with time series. The optimal model was selected to predict the postprandial feeding status of the subsequent round, serving as the first-stage model of feeding strategy. Eight models k-Nearest Neighbors, decision tree, support vector machine, random forests, adaBoost, gradient boosting decision trees, extreme gradient boosting, and light gradient boosting machine (LightGBM) were employed to construct feeding status classification models. The best classification performance was selected as the feeding determination model in the second stage. Results showed that the sensitive features included overall features, light intensity, water temperature, body mass, and per-round feeding rate. A classification between the original and sensitive features demonstrated the effectiveness of the sensitive features, according to gradient boosting decision trees and random forests models. Transformer-regression-classification (transformer-RC) model outperformed the rest of the models across 1 to 3-time windows, with the accuracy from 0.93 to 0.94. The Transformer-RC model effectively predicted the postprandial feeding status of the subsequent round. All five ensemble models achieved strong classification, which outperformed the three base models. Among them, the LightGBM model achieved an accuracy of 0.98 for inputs to the classification model of feeding status in the second stage of the feeding strategy. Both the Transformer-RC and LightGBM models shared high prediction and classification after validation, with average values of four evaluation metrics exceeding 0.89 under both feeding states, indicating strong generalization. This finding can provide a strong reference to develop intelligent feeding.