Sensor-Centric Intelligent Systems for Soybean Harvest Mechanization in Challenging Agro-Environments of China: A Review

Soybean-corn intercropping in the hilly-mountainous regions of Southwest China poses unique challenges to mechanized harvesting because of complex topography and agronomic constraints. Addressing the soybean-harvesting bottleneck in these fields requires advanced sensing and perception rather than purely mechanical redesigns. Prior reviews emphasized flat-terrain machinery or single-crop systems, leaving a gap in sensor-centric solutions for intercropping on steep, irregular plots. This review analyzes how sensors enable the next generation of intelligent harvesters by linking field constraints to perception and control. We frame the core failures of conventional machines-instability, inconsistent cutting, and low efficiency-as perception problems driven by low pod height, severe slope effects, and header-row mismatches. From this perspective, we highlight five fronts: (1) terrain-profiling sensors integrated with adaptive headers; (2) IMUs and inclination sensors for chassis stability and traction on slopes; (3) multi-sensor fusion of LiDAR and machine vision with AI for crop identification, navigation, and obstacle avoidance; (4) vision and spectral sensing for selective harvesting and impurity pre-sorting; and (5) acoustic/vibration sensing for low-damage, high-efficiency threshing and cleaning. We conclude that compact, intelligent machinery powered by sensing, data fusion, and real-time control is essential, while acknowledging technological and socio-economic barriers to deployment. This review outlines a sensor-driven roadmap for sustainable, efficient soybean harvesting in challenging terrains.