Deep Learning-Based Automated Detection of Tomato Leaf Diseases Using CNNs

Abstract

With the global prevalence of tomato diseases causing 20 to 40% annual crop losses and over USD 220 billion in economic damage, traditional manual scouting and laboratory diagnostics prove labor intensive, subjective, delayed, and impractical for resource constrained rural farmers. To address this challenge, this study proposes a lightweight 17 layer convolutional neural network (CNN) model enhanced by comprehensive data augmentation, effectively classifying nine prevalent tomato leaf diseases Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites, Target Spot, Yellow Leaf Curl Virus, and Mosaic Virus using the PlantVillage dataset of 16,012 images. The experiment utilized 80/20 train test splits with Adam optimizer (learning rate 0.001), categorical cross entropy loss, 50 epochs, and batch size 32. The proposed CNN was compared with pretrained InceptionV3 and ResNet152V2 baselines. Experimental results demonstrate the model achieves state of the art performance with 95.28% test accuracy, 97.80% training accuracy, 0.970 macro F1 score, 0.932 micro MCC, and 0.983 micro average AUC, outperforming InceptionV3 (81.54%) and ResNet152V2 (85.89%) by 13.74% and 9.39% respectively, while surpassing tomato specific SOTA VGG 19 (93%). Ablation experiments confirm augmentation yields 16.68% accuracy improvement over non augmented baselines. The model powers a React Native Android app with TensorFlow Lite INT8 quantization (7.1 MB), delivering sub 200 ms inference for online cloud analysis via FastAPI and offline edge computing, providing farmers real time diagnostics with robust generalization across diverse field conditions and significant practical value for precision agriculture and food security.