Extreme gradient boosting driven intelligent system for tomato leaf disease identification
DOI:
https://doi.org/10.24425/jppr.2026.3684Abstract
Tomato (Solanum lycopersicum) is an important vegetable crop which is susceptible to multiple diseases that can impact yield and produce quality. The current research aimed to classify major tomato diseases in the Caribbean region through a developed intelligent system. The system was based on six key diseases including bacterial spot, early blight, late blight, septoria leaf spot, yellow leaf curl virus, and mosaic virus. To improve the identification accuracy, all images were first resized and then segmented using the GrabCut method to isolate the leaf regions. The segmented images were subsequently converted to HSV color space and grayscale before feature extraction was performed. A total of 96 features were extracted, including color histograms, Local Binary Pattern (LBP), and Gray-Level Co-occurrence Matrix (GLCM). The Extreme Gradient Boosting (XGBoost) algorithm was employed for disease identification. This study used a dataset of 11,979 images from the PlantVillage collection, including both healthy and diseased samples. The developed model achieved a disease identification accuracy of 97.20% while remaining computationally efficient.
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