Tomato Leaf Disease Classification Using Proposed AlexNet: A Deep Learning Approach for Sustainable Agriculture

Abstract

Timely and accurate identification of tomato leaf diseases is important for reducing crop losses and supporting sustainable crop management. This study developed a modified AlexNet architecture for classifying field-captured tomato leaf images into seven disease categories. A custom dataset obtained under variable backgrounds and lighting conditions was expanded through rotation, horizontal and vertical flipping, and zooming to reduce class imbalance and improve model generalisation. The proposed architecture comprised three convolutional layers with 3 × 3 filters, reduced fully connected layers, and dropout regularisation to balance predictive performance and computational efficiency. The model was implemented using TensorFlow and Keras and trained for 30 epochs. Its performance was evaluated using accuracy and loss curves, a confusion matrix, receiver operating characteristic curves, area under the curve values, class-wise precision, recall and F1-scores, and Gradient-weighted Class Activation Mapping. Under the reported experimental protocol, the model achieved a validation accuracy of 99.74% and a macro-average F1-score of 0.8907. Bacterial spot and early blight showed comparatively strong classification performance, whereas target spot was the most difficult category to classify. Grad-CAM visualisations indicated that the model generally focused on symptom-bearing leaf regions. Comparisons with selected ResNet and VGG architectures showed favourable results for the proposed model on the same dataset. Independent external validation and deployment-oriented testing remain necessary before broader field applicability can be established.