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A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

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  • Mingfeng Huang

    (School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Guoqin Xu

    (School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Junyu Li

    (School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

  • Jianping Huang

    (School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China)

Abstract

Northern leaf blight (NLB) is a serious disease in maize which leads to significant yield losses. Automatic and accurate methods of quantifying disease are crucial for disease identification and quantitative assessment of severity. Leaf images collected with natural backgrounds pose a great challenge to the segmentation of disease lesions. To address these problems, we propose an image segmentation method based on YOLACT++ with an attention module for segmenting disease lesions of maize leaves in natural conditions in order to improve the accuracy and real-time ability of lesion segmentation. The attention module is equipped on the output of the ResNet-101 backbone and the output of the FPN. The experimental results demonstrate that the proposed method improves segmentation accuracy compared with the state-of-the-art disease lesion-segmentation methods. The proposed method achieved 98.71% maize leaf lesion segmentation precision, a comprehensive evaluation index of 98.36%, and a mean Intersection over Union of 84.91%; the average processing time of a single image was about 31.5 ms. The results show that the proposed method allows for the automatic and accurate quantitative assessment of crop disease severity in natural conditions.

Suggested Citation

  • Mingfeng Huang & Guoqin Xu & Junyu Li & Jianping Huang, 2021. "A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++," Agriculture, MDPI, vol. 11(12), pages 1-14, December.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1216-:d:693636
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    References listed on IDEAS

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    1. Prakhar Bansal & Rahul Kumar & Somesh Kumar, 2021. "Disease Detection in Apple Leaves Using Deep Convolutional Neural Network," Agriculture, MDPI, vol. 11(7), pages 1-23, June.
    2. Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
    3. Shuo Chen & Kefei Zhang & Yindi Zhao & Yaqin Sun & Wei Ban & Yu Chen & Huifu Zhuang & Xuewei Zhang & Jinxiang Liu & Tao Yang, 2021. "An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation," Agriculture, MDPI, vol. 11(5), pages 1-18, May.
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    Cited by:

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