Behavioral responses of group-housed pigs are strongly influenced by pigsty environmental conditions, yet their dynamic coupling is difficult to quantify under commercial farming scenarios. This difficulty arises from high inter-pig similarity, complex interactions, and rapidly changing environmental conditions, which pose significant challenges for existing vision-based multi-pig behavior detection and tracking methods. To address these challenges, this study proposes a PIG-Net–based dynamic coupling analysis framework that integrates behavior detection, multi-pig tracking, and behavior-environment interaction analysis. The model uses an EfficientRepBiFusion backbone with bidirectional feature fusion and a lightweight LSDGCD detection head, achieving mean Average Precision (mAP) of 93.5% for PIG YOLO on four pig behaviors—standing, dog-sitting, lateral lying, and prone lying. The integrated PIG-Net system achieves stable tracking performance with identification average rate (IDF1) of 90.7%, multiple object tracking accuracy (MOTA) of 88.6%, and a real-time processing speed of 26 FPS, while environmental sensors continuously record temperature, humidity, and CO₂ levels for long-term correlation analysis. Based on long-term monitoring, Pearson correlation analysis was applied to quantify the associations between pig behaviors and environmental factors, highlighting significant correlations with coefficients |r| ranging from 0.65 to 0.76. By combining these quantitative results with temporal and dimensionality reduction analyses, temperature, humidity, and CO₂ were identified as the primary environmental drivers. Active behaviors decreased under elevated temperature and humidity and increased during cooler and drier periods, whereas prone lying and lateral lying increased under thermal and moisture stress. Elevated CO₂ concentrations further suppressed activity, reflecting inhibitory effects of degraded air quality. These findings provide a quantitative basis for behavior-environment coupling assessment and early health warning in group-housed pigs.

Large high clearance self-propelled sprayers were widely used in field plant protection due to their high-efficiency operation capabilities. Influenced by the characteristics of field operations such as high power, heavy weight, high ground clearance, and fast operation speed, the comprehensive requirements for the ride comfort, handling stability and road friendliness of the sprayer were increasingly strong. At the present stage, the chassis structure of the high clearance self-propelled sprayer that attaches great importance to the improvement of comprehensive performance still has the problems of severe bumps, weak handling performance and serious road damage in complex field environments. Therefore, this paper proposes an optimization design method for hydro-pneumatic suspension system of a high clearance self-propelled sprayer based on the improved MOPSO (Multi-Objective Particle Swarm Optimization) algorithm, covering the entire process of configuration design, parameter intelligent optimization, and system verification of the high clearance self-propelled sprayer chassis. Specifically, chassis structure of the hydro-pneumatic suspension suitable for the high clearance self-propelled sprayer was designed, and a design method combining the improved MOPSO algorithm based on time-varying fusion strategy and adaptive update with the parameter optimization of hydro-pneumatic suspension based on this algorithm was proposed, and finally the software simulation and bench performance verification were carried out. The results show that the optimized hydro-pneumatic suspension has excellent vibration reduction effect, and the body acceleration, suspension dynamic deflection and tire deflection were increased by 16.5%, 9.9% and 0.9% respectively, compared with those before optimization. The comprehensive performance of the hydro-pneumatic suspension designed in this study is better than that of the traditional suspension.