A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1x1, which reduces the number of parameters and helps inmore detailed and descriptive feature extraction for classification. The proposedDTomatoDNetmodel is trained from scratch to determine the classification success rate. 10,000 tomato leaf images (1000 images per class) from the publicly accessible dataset, covering one healthy category and nine disease categories, are utilized in training the proposed DTomatoDNet approach. More specifically, we classified tomato leaf images into Target Spot (TS), Early Blight (EB), Late Blight (LB), Bacterial Spot (BS), LeafMold (LM), Tomato Yellow Leaf Curl Virus (YLCV), Septoria Leaf Spot (SLS), Spider Mites (SM), Tomato Mosaic Virus (MV), and Tomato Healthy (H). The proposedDTomatoDNet approach obtains a classification accuracy of 99.34%, demonstrating excellent accuracy in differentiating between tomato diseases. Themodel could be used onmobile platforms because it is lightweight and designed with fewer layers. Tomato farmers can utilize the proposed DTomatoDNetmethodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.