大田智能农机关键作业参数感知与检测方法综述OA

The rational selection and reliable acquisition of key operational parameters constitute a fundamental basis for enabling autonomous operation,intelligent decision-making,and precise control in large-scale smart agricultural machinery.They play a critical role in supporting the enhancement of perception capabilities and control efficiency under complex field operation conditions.Due to the variability of operational environments and significant differences in working mechanisms,challenges persist in the parameter acquisition process of large-scale smart agricultural machinery.These included data redundancy,insufficient coupling between perception results and control units,and limited system integration and generalizability,which constrained the further improvement of operational decision accuracy and overall machine performance.The types of key operational parameters,sensing,and primary detection methods were systematically reviewed across four major stages of field operations:tillage,seeding,crop management,and harvesting.Relevant domestic and international research findings,as well as technological solutions of global smart agricultural machinery,were critically assessed.Based on this review,existing issues in parameter acquisition methods were summarized,and analyses were conducted from the perspectives of parameter system construction,coordination between sensing and control,and system integration.Finally,in light of the development trends in smart agricultural machinery,future research directions were identified,including multi-source data selection and fusion,design of high-robustness sensing systems,cross-platform data sharing and standardization,and optimization of parameter-driven control strategies.These directions were expected to guide the development of efficient perception systems for key operational parameters in large-scale smart agricultural machinery.