Tomato Leaf Disease Classification Using the K-Nearest Neighbor Method with Watershed Segmentation

Abstract

Tomato leaf diseases are one of the main factors causing a decline in tomato crop productivity and agricultural yields. Manual identification of leaf diseases is subjective, time-consuming, and prone to inconsistency. Therefore, this study proposes an automated tomato leaf disease classification system based on digital image processing and machine learning by combining watershed segmentation, HSV color features, Gray Level Co-occurrence Matrix (GLCM) texture features, and the K-Nearest Neighbor (KNN) algorithm. The dataset used consists of 8,111 single-leaf tomato images covering nine disease classes and one healthy class, as well as an additional multi-leaf dataset for segmentation evaluation. The research process includes image preprocessing, leaf segmentation using the watershed method, feature extraction using HSV and GLCM, and classification using KNN with Euclidean distance. Experimental results show that the proposed system achieved an accuracy of 87.08% on training data, 82.96% on validation data, and 81.98% on test data. Furthermore, the watershed segmentation method successfully separated individual leaves in multi-leaf images, enabling effective per-leaf classification. These results indicate that the combination of handcrafted features and the KNN algorithm remains effective and computationally efficient for tomato leaf disease classification tasks.