深度实例分割方法在农作物观测领域的应用

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中图分类号：TP391.4 文献标识码：A 文章编号：2095-5553（2026）02-0177-11

Abstract：Agriculture is animportant partoftheglobal economy，and the developmentof precisionagricultureisof great significanceto improving cropyieldandqualityand reducing resource consumption.In recent years，theaplication of deep learning，especiallyinstance segmentation methods，hasmadesignificant progress inthe fieldofcrop observation. This article describes the latestresearch progress in theapplicationof instance segmentation methods in croposervation， covering avarietyof applicationscenarios fromcrop phenotypic characteristic analysis，crop pest and disease detection， fruitidentificationand management，toweed identificationand management，cropgrowth monitoring，etc.A brief descriptionof theinstancesegmentationmethodsisprovidedfromtheaspectsof development history，representative methods，etc.Thenthe practical applicationof theinstance segmentation methodis introduced indetail bycategoryand scenario．Finally，thetechnicalchallenges itfacesareanalyzed，especiallydatasetlimitations，identificationaccuracyin complexenvironments，real-timeperformanceandcomputingresources，model generalizationcapabilities，andcrossmodal andheterogeneous data processing capabilities.Based onthis.Solutions such asenrichingand expanding data sets， optimizing lightweight modelsand algorithms，multimodaldata fusion，innovative aplication ofdeep learning technology， and integrated application of intelligent agricultural systems are proposed.

Keywords：instance segmentation；deep learning；precision agriculture；crop phenotyping；disease detection

0 引言

农业是人类赖以生存和发展的基础产业，精准农业是保障粮食安全的重要途径。（剩余24759字）