Prediction of Tomato Leaf Diseases using Computational Convolution Neural Network Method

The growing world population has led to a rising demand for food. Tomatoes, a staple in global diets, are widely used in fresh consumption, sauces and processed products. However, tomato diseases pose a major challenge, significantly reducing yield and crop quality. Timely and accurate identification of these diseases is essential for effective management. This study uses advancements in deep learning to develop a Convolutional Neural Network model. The model classifies tomato leaf conditions into five categories: Healthy leaves (HC), two-spotted spider mite (TSSM), tomato yellow leaf curl disease (TYLCD), tomato mosaic virus (ToMV) and target spot (TS).

The study utilized a publicly available dataset from Mendeley. Images were preprocessed by resizing, reorienting and enhancing quality, ensuring compatibility with the CNN model. The dataset was divided into an 80:20 ratio for training and validation. The CNN architecture consisted of six convolutional layers with ReLU activation, max-pooling layers and two fully connected layers for classification. Model performance was evaluated using Precision, Recall, F1-score and accuracy metrics.

The CNN model achieved a training accuracy of 93.51% and a validation accuracy of 94.83%. TYLCD had the highest classification precision (98.67%) and recall (99.8%). Overall model accuracy was 95.7%, with macro-average and weighted-average F1-scores of 0.9264 and 0.9567, respectively. The confusion matrix highlighted TYLCD as the most accurately classified disease, while TSSM showed the highest misclassification rate. These results demonstrate the model’s potential for reliable tomato disease identification, supporting precision agriculture practices.