福建农业学报2024，Vol.39Issue(2)：199-205,7.DOI:10.19303/j.issn.1008-0384.2024.02.010
基于深度学习的蔬菜田精准除草作业区域检测方法
Deep Learning Detection of Weeds in Vegetable Fields
摘要
Abstract
[Objective]Deep learning to accurately identify weeds for effective weeding in vegetable fields was investigated.[Method]Image of a vegetable field was cropped into grid cells as sub-images of vegetables,weeds,and bare ground.Deep learning networks using the ShuffleNet,DenseNet,and ResNet models were applied to distinguish the target sub-images,particularly the areas required weeding.Precision,recall rate,F1 score,and overall and average accuracy in identifying weeds of the models were evaluated.[Result]Although all applied models satisfactorily distinguished weeds from vegetables,ShuffleNet could simultaneously deliver a 95.5%precision with 97%recall and a highest detection speed of 68.37 fps suitable for real-time field operations.[Conclusion]The newly developed method using the ShuffleNet model was feasible for precision weed control in vegetable fields.关键词
蔬菜/杂草/图像处理/深度学习/作业区域检测Key words
Vegetables/weeds/image treatment/deep learning/weeding area determination分类
信息技术与安全科学引用本文复制引用
李卫丽,金小俊,于佳琳,陈勇..基于深度学习的蔬菜田精准除草作业区域检测方法[J].福建农业学报,2024,39(2):199-205,7.基金项目
国家自然科学基金项目(32072498) （32072498）
江苏省研究生科研与实践创新计划项目(KYCX22_1051) （KYCX22_1051）
