Automated Detection of Tomato Leaf Diseases using Convolutional Neural Networks (CNNs)

Abstract

The agricultural sector plays a crucial role in ensuring global food security, with tomatoes being a significant crop contributing to both economic and nutritional aspects. However, the presence of diseases can severely impact tomato yield and quality. This study proposes an automated approach for the early detection of tomato leaf diseases using Convolutional Neural Networks (CNNs). The proposed system leverages the power of deep learning, particularly CNNs, to analyze images of tomato leaves and accurately identify the presence of diseases. A comprehensive dataset containing diverse images of healthy and diseased tomato leaves is utilized for training and validation purposes. The CNN model is trained to learn distinctive features and patterns associated with various tomato leaf diseases, enabling it to make precise predictions. The methodology involves pre-processing the images to enhance their quality, followed by the construction and training of the CNN model. The trained model is then evaluated on a separate test dataset to assess its generalization capabilities. The performance metrics, including accuracy, precision, recall, and F1 score, are employed to quantify the effectiveness of the automated detection system. The results demonstrate the efficacy of the proposed CNN-based approach in accurately identifying tomato leaf diseases. The system exhibits a high level of accuracy and reliability, showcasing its potential as a valuable tool for farmers and agricultural stakeholders. The automation of disease detection not only facilitates early intervention and treatment but also contributes to the overall improvement of crop management practices, leading to enhanced agricultural productivity and sustainable food production