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Channel–Spatial Segmentation Network for Classifying Leaf Diseases

Author

Listed:
  • Balaji Natesan

    (College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei City 106, Taiwan)

  • Anandakumar Singaravelan

    (Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Jia-Lien Hsu

    (Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Yi-Hsien Lin

    (Department of Plant Medicine, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Baiying Lei

    (National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China)

  • Chuan-Ming Liu

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei City 106, Taiwan)

Abstract

Agriculture is an important resource for the global economy, while plant disease causes devastating yield loss. To control plant disease, every country around the world spends trillions of dollars on disease management. Some of the recent solutions are based on the utilization of computer vision techniques in plant science which helps to monitor crop industries such as tomato, maize, grape, citrus, potato and cassava, and other crops. The attention-based CNN network has become effective in plant disease prediction. However, existing approaches are less precise in detecting minute-scale disease in the leaves. Our proposed Channel–Spatial segmentation network will help to determine the disease in the leaf, and it consists of two main stages: (a) channel attention discriminates diseased and healthy parts as well as channel-focused features, and (b) spatial attention consumes channel-focused features and highlights the diseased part for the final prediction process. This investigation forms a channel and spatial attention in a sequential way to identify diseased and healthy leaves. Finally, identified leaf diseases are divided into Mild, Medium, Severe, and Healthy. Our model successfully predicts the diseased leaves with the highest accuracy of 99.76%. Our research study shows evaluation metrics, comparison studies, and expert analysis to comprehend the network performance. This concludes that the Channel–Spatial segmentation network can be used effectively to diagnose different disease degrees based on a combination of image processing and statistical calculation.

Suggested Citation

  • Balaji Natesan & Anandakumar Singaravelan & Jia-Lien Hsu & Yi-Hsien Lin & Baiying Lei & Chuan-Ming Liu, 2022. "Channel–Spatial Segmentation Network for Classifying Leaf Diseases," Agriculture, MDPI, vol. 12(11), pages 1-20, November.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1886-:d:968132
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    References listed on IDEAS

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    1. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    2. Iftikhar Ahmad & Muhammad Hamid & Suhail Yousaf & Syed Tanveer Shah & Muhammad Ovais Ahmad, 2020. "Optimizing Pretrained Convolutional Neural Networks for Tomato Leaf Disease Detection," Complexity, Hindawi, vol. 2020, pages 1-6, September.
    3. Ozguven, Mehmet Metin & Adem, Kemal, 2019. "Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    4. Umesh Kumar Lilhore & Agbotiname Lucky Imoize & Cheng-Chi Lee & Sarita Simaiya & Subhendu Kumar Pani & Nitin Goyal & Arun Kumar & Chun-Ta Li, 2022. "Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification," Mathematics, MDPI, vol. 10(4), pages 1-19, February.
    5. Gary Storey & Qinggang Meng & Baihua Li, 2022. "Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture," Sustainability, MDPI, vol. 14(3), pages 1-14, January.
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