Intelligent Sensing and Adaptive Control for Low-Loss Soybean Harvesting: A Review of Multi-Scenario Technologies

Abstract

Soybean harvesting is strongly affected by low pod position, lodging, pod shattering, moisture variation, and changing material flow, making stable low-loss operation difficult across diverse field conditions. This review synthesizes 179 core publications identified through structured literature searches and citation tracking, including 69 direct soybean studies, six adjacent-legume studies, and 104 cross-crop technology-transfer studies. The literature is organized around a sensing–decision–actuation–feedback chain covering harvest-loss monitoring, grain-quality assessment, crop and terrain perception, navigation, and adaptive regulation. Low-loss header, threshing, separation, and cleaning technologies are examined as the physical processes through which sensing and control affect harvest-quality outcomes. The synthesis shows that future progress requires robust multi-source sensing under dust, vibration, illumination variation, and crop occlusion; field-calibrated state estimation; coordinated multi-actuator closed-loop control; standardized operating and quality databases; and validation across cultivars, terrains, and cropping systems. Representative soybean studies reviewed here reported reductions of approximately 30.4% in grain breakage, 62.6% in harvest loss, and 50.0% in cleaning loss under their respective experimental conditions.