BOUNDARY-BASED RICE-LEAF-DISEASE CLASSIFICATION AND SEVERITY LEVEL ESTIMATION FOR AUTOMATIC INSECTICIDE INJECTION
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Publication Details
Author list: Tepdang S., Chamnongthai K.
Publisher: American Society of Agricultural and Biological Engineers
Publication year: 2023
Journal acronym: ASABE
Volume number: 39
Issue number: 3
Start page: 367
End page: 379
Number of pages: 13
ISSN: 0883-8542
eISSN: 1943-7838
Languages: English-Great Britain (EN-GB)
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Abstract
Farmers may decide to select an appropriate insecticide for rice-leaf disease treatment in a paddy rice field based on disease class and severity level. To classify the class of rice leaf disease and estimate the severity level in a paddy rice field, several parts of the rice leaf are included in a captured image, and sometimes there exists more than one disease boundary in a part of rice leaf. This article proposes a method of rice-leaf disease classification and severity level estimation for multiple diseases on a multiple rice-leaf image. This method first finds rice-leaf candidate boundaries and identifies the rice leaf based on its feature of color, shape, and area ratio. To enlarge classification tolerance based on the coarse-to-fine concept, disease candidate boundaries are categorized into two major groups in the coarse level, and then both groups are classified into rice leaf classes in the fine level. To evaluate the performance of the proposed method, experiments were performed with 8,303 images of three rice leaf diseases including brown spot, rice blast, rice hispa and healthy rice leaf, and our proposed method achieved 99.27% which outperformed the deep learning approach by 0.43%. © 2023 American Society of Agricultural and Biological Engineers.
Keywords
Coarse to fine, Multiple rice-leaf diseases, Rice-leaf disease recognition
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