Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm

Xinle Zhang (), Jian Cui, Huanjun Liu, Yongqi Han, Hongfu Ai, Chang Dong, Jiaru Zhang and Yunxiang Chu
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Xinle Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jian Cui: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Huanjun Liu: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Yongqi Han: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Hongfu Ai: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Chang Dong: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Jiaru Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yunxiang Chu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2023, vol. 13, issue 1, 1-16

Abstract: Soybean in the field has a wide range of intermixed weed species and a complex distribution status, and the weed identification rate of traditional methods is low. Therefore, a weed identification method is proposed based on the optimized Faster R-CNN algorithm for the soybean seedling. Three types of weed datasets, including soybean, with a total of 9816 photos were constructed, and cell phone photo data were used for training and recognition. Firstly, by comparing the classification effects of ResNet50, VGG16, and VGG19, VGG19 was identified as the best backbone feature extraction network for model training. Secondly, an attention mechanism was embedded after the pooling layer in the second half of VGG19 to form the VGG19-CBAM structure, which solved the problem of low attention to the attention target during model training using the trained Faster R-CNN algorithm to identify soybean and weeds in the field under the natural environment and compared with two classical target detection algorithms, SSD and Yolov4. The experimental results show that the Faster R-CNN algorithm using VGG19-CBAM as the backbone feature extraction network can effectively identify soybeans and weeds in complex backgrounds. The average recognition speed for a single image is 336 ms, and the average recognition accuracy is 99.16%, which is 5.61% higher than before optimization, 2.24% higher than the SSD algorithm, and 1.24% higher than the Yolov4 algorithm. Therefore, this paper’s optimized target detection model is advantageous and can provide a scientific method for accurate identification and monitoring of grass damage.

Keywords: soybean weed; target detection; feature extraction network; Faster R-CNN; attention mechanism (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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