Deep learning-based weed recognition model in the maize field

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Deep learning-based weed recognition model in the maize field

Journal Title: Journal of Henan Agricultural University - Year 2024, Vol 58, Issue 2

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. [Conclusion] The test results show that the mAP value of YOLOv5 model is 0.961, and all of its indexes are better than those of YOLOv3 and SSD target detection model, so YOLOv5 model is more suitable for the automated operation of accurate herbicide spraying in crop field.

Authors and Affiliations

Bingjie LIU, Yanan ZHOU, Xiaohui ZHOU, Li DING, He LI, Wanzhang WANG

Keywords

deep learning corn weed recognition target detection model

Distribution characteristics and influencing factors of soil organic carbon in the riparian zone of Mengjin Section of the Yellow River

[Objective] The purpose of this study is to investigate the differences in the spatial distribution of soil organic carbon in the riparian zone of the Mengjin section of the Yellow River, and to provide a theoretical bas...

EP ID EP769338
DOI 10.16445/j.cnki.1000-2340.20231110.002
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