Automatic weed identification and detection are crucial for precision weeding operations.In recent years,deep learning(DL)has gained widespread attention for its potential in crop weed identification.This paper provid...Automatic weed identification and detection are crucial for precision weeding operations.In recent years,deep learning(DL)has gained widespread attention for its potential in crop weed identification.This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL.Through an analysis of relevant literature from both within and outside of China,the author summarizes the development history,research progress,and identification and detection methods of DL-based weed identification technology.Emphasis is placed on data sources and DL models applied to different technical tasks.Additionally,the paper discusses the challenges of time-consuming and laborious dataset preparation,poor generality,unbalanced data categories,and low accuracy of field identification in DL for weed identification.Corresponding solutions are proposed to provide a reference for future research directions in weed identification.展开更多
基金supported by the Top Talents Program for One Case,One Discussion of Shandong Province([2018]27 of the Shandong Provincial Government Office)Natural Science Foundation of Shandong Province(Grant No.ZR2021 QC154)the international cooperation project of the China Scholarship Council for cultivating innovative talents(Grant No.202201040005).
文摘Automatic weed identification and detection are crucial for precision weeding operations.In recent years,deep learning(DL)has gained widespread attention for its potential in crop weed identification.This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL.Through an analysis of relevant literature from both within and outside of China,the author summarizes the development history,research progress,and identification and detection methods of DL-based weed identification technology.Emphasis is placed on data sources and DL models applied to different technical tasks.Additionally,the paper discusses the challenges of time-consuming and laborious dataset preparation,poor generality,unbalanced data categories,and low accuracy of field identification in DL for weed identification.Corresponding solutions are proposed to provide a reference for future research directions in weed identification.
