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Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network

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  • Guoqing Feng

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Cheng Wang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Aichen Wang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yuanyuan Gao

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yanan Zhou

    (Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Shuo Huang

    (Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Bin Luo

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

Abstract

Crop lodging is an important cause of direct economic losses and secondary disease transmission in agricultural production. Most existing methods for segmenting wheat lodging areas use a large-volume network, which poses great difficulties for annotation and crop monitoring in real time. Therefore, an ultra-lightweight model, Lodging-U2NetP (L-U2NetP), based on a novel annotation strategy which crops the images before annotating them (Crop-annotation), was proposed and applied to RGB images of wheat captured with an unmanned aerial vehicle (UAV) at a height of 30 m during the maturity stage. In the L-U2NetP, the Dual Cross-Attention (DCA) module was firstly introduced into each small U-structure effectively to address semantic gaps. Then, Crisscross Attention (CCA) was used to replace several bulky modules for a stronger feature extraction ability. Finally, the model was compared with several classic networks. The results showed that the L-U2NetP yielded an accuracy, F1 score, and IoU (Intersection over Union) for segmenting of 95.45%, 93.11%, 89.15% and 89.72%, 79.95%, 70.24% on the simple and difficult sub-sets of the dataset (CA set) obtained using the Crop-annotation strategy, respectively. Additionally, the L-U2NetP also demonstrated strong robustness in the real-time detection simulations and the dataset (AC set) obtained using the mainstream annotation strategy, which annotates images before cropping (Annotation-crop). The results indicated that L-U2NetP could effectively extract wheat lodging and the Crop-annotation strategy provided a reliable performance which is comparable with that of the mainstream one.

Suggested Citation

  • Guoqing Feng & Cheng Wang & Aichen Wang & Yuanyuan Gao & Yanan Zhou & Shuo Huang & Bin Luo, 2024. "Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network," Agriculture, MDPI, vol. 14(2), pages 1-16, February.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:2:p:244-:d:1331530
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    References listed on IDEAS

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    1. Shengyi Zhao & Yun Peng & Jizhan Liu & Shuo Wu, 2021. "Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
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