Weed Detection in Peanut Fields Based on Machine Vision

Hui Zhang, Zhi Wang, Yufeng Guo, Ye Ma, Wenkai Cao, Dexin Chen, Shangbin Yang and Rui Gao ()
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Hui Zhang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Zhi Wang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Yufeng Guo: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Ye Ma: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Wenkai Cao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Dexin Chen: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Shangbin Yang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Rui Gao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Agriculture, 2022, vol. 12, issue 10, 1-15

Abstract: The accurate identification of weeds in peanut fields can significantly reduce the use of herbicides in the weed control process. To address the identification difficulties caused by the cross-growth of peanuts and weeds and by the variety of weed species, this paper proposes a weed identification model named EM-YOLOv4-Tiny incorporating multiscale detection and attention mechanisms based on YOLOv4-Tiny. Firstly, an Efficient Channel Attention (ECA) module is added to the Feature Pyramid Network (FPN) of YOLOv4-Tiny to improve the recognition of small target weeds by using the detailed information of shallow features. Secondly, the soft Non-Maximum Suppression (soft-NMS) is used in the output prediction layer to filter the best prediction frames to avoid the problem of missed weed detection caused by overlapping anchor frames. Finally, the Complete Intersection over Union (CIoU) loss is used to replace the original Intersection over Union (IoU) loss so that the model can reach the convergence state faster. The experimental results show that the EM-YOLOv4-Tiny network is 28.7 M in size and takes 10.4 ms to detect a single image, which meets the requirement of real-time weed detection. Meanwhile, the mAP on the test dataset reached 94.54%, which is 6.83%, 4.78%, 6.76%, 4.84%, and 9.64% higher compared with YOLOv4-Tiny, YOLOv4, YOLOv5s, Swin-Transformer, and Faster-RCNN, respectively. The method has much reference value for solving the problem of fast and accurate weed identification in peanut fields.

Keywords: weed identification; YOLOv4-Tiny; attention mechanism; multiscale detection; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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