A Machine Learning–Driven Framework for Early Detection and Classification of Tomato Leaf Diseases to Enhance Agricultural Productivity and Crop Health

Abstract

Tomato is a global important horticultural crop whose yield and quality are severely affected by bacterial, fungal and viral pathogens that incite foliar diseases. As such, early and accurate diagnosis of such ills is critical for crop management. Traditional methods for diagnosis fell to manual inspection and laboratory analysis are burdensome and time-consuming and impractical in large-scale and resource-constrained agricultural environments. Although recent efforts in deep learning and computer vision have led to automated diagnosis of plant disease, a lot of current approaches are based on laboratory-curated datasets and lack robustness, interpretability or deploy ability under real state conditions. This manuscript proposes a complete framework based on deep learning for the early detection and classification of tomato leaf diseases which simultaneously addresses the problem of accuracy, generalization, explainability, and deployment feasibility. The system exploits transfer learning using state-of-the-art convolutional neural network architectures such as EfficientNetB4, ResNet50, InceptionV3 and MobileNetV3 refined with a combination of laboratory and acquired image datasets collected in field. To counter-act the class imbalance and environmental variability we use plenty of data augmentation, normalization and regularization protocols. The models are evaluated based on a set of stringent performance results such as accuracy, precision, recall, F1-score and AUC. Experimental results show that our model which is effectively based on the EfficientNetB4 model outperforms the competing models with an accuracy of classification ranging from 96 percent to 99 percent, an eurointiention range of almost 0.99, while at the same time ensuring a robust generalization of the results under field-like conditions. Lightweight architectures like MobileNetV3 also help in enabling real-time inference on edge devices making the system practical. In sum, the proposed framework presents a solution that is scalable and interpretable and which can be easily deployed to serve as a solution for precision agriculture in favor of improved disease management, crop resilience fortification and sustainable tomato production.