An Integrated Feature-Deep Learning Framework for Tomato Leaf Disease Classification and Infection Localization
Year: 2026
Type: article
Abstract: Tomato plants are susceptible to various diseases that significantly impact crop yield and quality. Accurate and timely identification of these diseases is crucial for effective management and mitigation. This study presents a deep learning-based methodology for enhancing disease prediction, classification, and precise localization of affected areas within tomato leaves. The proposed approach leverages a combination of statistical, texture (Tamura and GLCM), geometry, and color features extracted from leaf images. To further enrich feature representation, wavelet analysis is employed. Besides just identifying ten common tomato leaf diseases, our model also figures out how much of the leaf is actually affected, which helps in judging how serious the disease is. We tested it on a set of 10,000 images, and it hit an accuracy of around 99.5%, which is pretty solid. This kind of performance shows that the model can really help farmers and researchers catch issues early and take action before things get worse.
Authors: Pratik Buchke, A. V.R. Mayuri
Institution: VIT Bhopal University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W7204242010