Rice-Disease Severity Level Estimation Using Deep Convolutional Neural Network

Conference proceedings article

Authors/Editors

KOSIN CHAMNONGTHAI

Strategic Research Themes

Digital Transformation (Strategic Research Themes)

Publication Details

Author list: Tendang S., Chamnongthai K.

Publisher: Elsevier

Publication year: 2021

ISBN: 9781665435536

ISSN: 0928-4931

eISSN: 1873-0191

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113982672&doi=10.1109%2fITC-CSCC52171.2021.9501485&partnerID=40&md5=4eb109781c1a34eb8533c29c9d39bc1e

Languages: English-Great Britain (EN-GB)

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Abstract

Severity of rice disease is an important indicator for farmers to plan appropriate treatments for protecting diseases which may give damages to rice paddy fields. This paper proposes an estimation method of rice-disease severity level using deep convolutional neural network. Since rice-disease severity level is the ratio between rice disease area and whole area in a rice leaf, candidate boundaries of rice disease are detected, and those boundaries are classified into a level out of rice-leaf disease levels which are early, middle, and final stages. All classified boundaries of levels are calculated with total area, and finally ratio with the whole leaf area is obtained. To evaluate performance of the proposed method, experiments with 2,500 images and 5,000 disease boundaries including five disease types have been performed, and results reveal 96.40%, 96.40%, and 96.56% accuracy for early, middle, and final stages, respectively. © 2021 IEEE.

Keywords

Disease Boundaries, Severity level