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.

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.

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.

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.

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.

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.

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.

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.

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.

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.