Smart Farming in Bangladesh: Mobile Application for Tomato Leaf Disease Detection Using a Hybrid VGG16-CNN Model

Tomato cultivation (Solanum lycopersicum L.) is highly significant due to its considerable economic value, high consumer demand, and critical role in supporting the livelihoods of farmers in Bangladesh. However, the majority of Bangladeshi farmers rely on traditional, manual methods for detecting tomato leaf diseases, relying on visual inspection and personal experience. Limited resources and a lack of awareness about advanced technologies further hinder the adoption of efficient disease detection methods. Computer vision, a cutting-edge technology, enables the automated identification and classification of tomato leaf diseases, holding significant promise for improving agricultural productivity and farmers’ livelihoods. This study focuses on developing a robust disease detection framework involving image acquisition, preprocessing, and feature extraction using a VGG16-CNN hybrid model, integrated with smartphone applications for real-time detection. To address the limitations faced by local farmers and plant enthusiasts unfamiliar with such technology, a diverse dataset of approximately 16,824 images was created, comprising field images and online sources. The proposed method leverages VGG16 for feature extraction, achieving enhanced performance through additional fine-tuned layers that form a hybrid model. This approach delivers an accuracy of 98%, with an F1 score of 98%. These findings highlight the potential of the proposed system to significantly mitigate the impacts of tomato leaf diseases, thereby improving tomato cultivation and production outcomes.