Tea leaf disease detection using multi-objective image segmentation

• Published in: Multimedia tools and applications Vol. 80; no. 1; pp. 753 - 771
• Main Authors: Mukhopadhyay, Somnath, Paul, Munti, Pal, Ramen, De, Debashis
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V
• Subjects: Clustering; Computer Communication Networks; Computer Science; Crop yield; Data Structures and Information Theory; Disease; Genetic algorithms; Image processing; Image segmentation; Medical imaging; Multimedia Information Systems; Multiple objective analysis; Plant diseases; Sorting algorithms; Special Purpose and Application-Based Systems; Tea; Multi-class SVM; NSGA-II based image clustering; Feature reduction; Tea leaf disease detection; PCA
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09567-1

Tea leaves’ diseases caused by constant exposure to pathogens lead to significant crop yield loss globally. Diagnosing the tea leave disease at an early stage minimizes the tea yield loss. In this study, a novel approach is presented for automatically detecting tea leaves diseases based on image processing technology. The Non-dominated Sorting Genetic Algorithm (NSGA-II) based image clustering is proposed for detecting the disease area in tea leaves. After that, PCA and multi-class SVM is used for feature reduction and identifying the disease in the tea leaves, respectively. The result shows that the proposed algorithm can detect the type of disease persisting in tea leaves with an average accuracy of 83%. Five different tea leaf diseases are considered here, such as Red Rust, Red Spider, Thrips, Helopeltis, and Sunlight Scorching.