河南农业大学学报2024，Vol.58Issue(2)：279-286,8.DOI:10.16445/j.cnki.1000-2340.20231110.002
基于深度学习的玉米田间杂草识别模型研究
Deep learning-based weed recognition model in the maize field
摘要
Abstract
[Objective]In response to the complexity and low accuracy of existing field weed recognition models,a corn field weed identification algorithm is studied.By accurately identifying weed images,theoretical and technical support is provided to improve the effectiveness of field weed control.[Method]In this paper,based on deep learning method,four types of common weeds in maize field,bluegrass,chenopodium album,clrsumsetosum and sedge were selected as experimental data sets,and the YOLOv3,YOLOv5 and SSD target detection models were established and trained.[Result]The results showed that the YOLOv3 model achieved precision of 0.734,mean recall of 0.814,mean F1 score of 0.789,and mAP value of 0.972;the YOLOv5 model achieved precision of 0.914,mean recall of 0.967,mean F1 score of 0.942 and mAP of 0.961;the mAP value of the SSD model is 0.907.[Conclu-sion]The test results show that the mAP value of YOLOv5 model is 0.961,and all of its indexes are bet-ter than those of YOLOv3 and SSD target detection model,so YOLOv5 model is more suitable for the au-tomated operation of accurate herbicide spraying in crop field.关键词
深度学习/玉米/杂草识别/目标检测模型Key words
deep learning/corn/weed recognition/target detection model分类
农业工程引用本文复制引用
刘冰杰,周雅楠,周小辉,丁力,李赫,王万章..基于深度学习的玉米田间杂草识别模型研究[J].河南农业大学学报,2024,58(2):279-286,8.基金项目
国家现代农业产业技术体系建设专项项目(CARS-04-PS28) （CARS-04-PS28）
河南省科技攻关项目(232102211087,222102110032) （232102211087,222102110032）
河南省科技研发计划联合基金项目(232103810019) （232103810019）
