农作物苗情检测技术研究进展与展望OA

Crop seedling detection is a crucial technology in precision agriculture for sensing early growth status and supporting precise field management.On the basis of clarifying key seedling condi-tion parameters and technical systems,this paper focuses on data acquisition platforms,multi-source sensing data,and intelligent detection methods,systematically reviewing research progress in plant recognition and emergence counting,growth phenotyping extraction,and diagnosis of diseases,pests,weeds,and stress conditions.Key challenges are analyzed from the perspectives of data foundation,model generalization,multi-source fusion,edge deployment,and agronomic integration.Future devel-opment directions are also discussed,including standard dataset construction,robust learning,tempo-ral fusion,cloud-edge collaboration,and closed-loop system of"perception-decision".This review aims to provide references for technology selection,method optimization,and field application in crop seedling detection.