Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

Abstract

This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.