Published Paper Details:

Tomato cultivation plays a crucial role in global agriculture and economy; however, it is often challenged by various diseases affecting plant health and yield. Timely detection and management of these diseases are vital for ensuring crop productivity and food security. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising results in automating disease detection tasks in plants. This study proposes a novel approach for detecting tomato leaf diseases utilizing CNNs. The proposed model leverages the power of deep learning to automatically learn discriminative features from input images, enabling accurate classification of healthy and diseased tomato leaves. The dataset used for training and evaluation comprises a diverse range of tomato leaf images representing different disease classes. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying various tomato leaf diseases, outperforming traditional machine learning approaches and achieving competitive performance compared to existing deep learning-based methods. The developed model offers a valuable tool for early and accurate diagnosis of tomato leaf diseases, facilitating prompt intervention and effective management strategies for ensuring sustainable tomato production.