Channel–Spatial Segmentation Network for Classifying Leaf Diseases

Balaji Natesan, Anandakumar Singaravelan, Jia-Lien Hsu, Yi-Hsien Lin, Baiying Lei () and Chuan-Ming Liu ()
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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

Agriculture, 2022, vol. 12, issue 11, 1-20

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.

Keywords: plant disease; channel attention; spatial attention; evaluation metrics; CBAM; disease degree (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: 2022
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