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.