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

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 pro...