No abstract

Highlights A rice-leaf-disease detection and classification algorithm for multiple rice-leaf-diseases in a complicated rice leaf image is proposed in this article. To increase rice-leaf-disease classification accuracy, an algorithm for coarse-to-fine determination is proposed. Since features of rice-leaf-disease types such as color, shape, and so on are similar and difficult to classify even with the human eye, tolerances among those features are small. The algorithm considers enlarging the tolerances using two-step classification of coarse-to-fine. Severity level of rice leaf disease is also estimated in our proposed method. 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%. Keywords: Coarse to fine, Multiple rice-leaf diseases, Rice-leaf disease recognition, Severity level.