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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xinle Zhang & Jian Cui & Huanjun Liu & Yongqi Han & Hongfu Ai & Chang Dong & Jiaru Zhang & Yunxiang Chu, 2023. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:175-:d:1031008
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    References listed on IDEAS

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    2. Lifang Fu & Xingchen Lv & Qiufeng Wu & Chengyan Pei, 2020. "Field Weed Recognition Based on an Improved VGG With Inception Module," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 11(2), pages 1-13, April.
    3. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    4. Hui Zhang & Zhi Wang & Yufeng Guo & Ye Ma & Wenkai Cao & Dexin Chen & Shangbin Yang & Rui Gao, 2022. "Weed Detection in Peanut Fields Based on Machine Vision," Agriculture, MDPI, vol. 12(10), pages 1-15, September.
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