Modeling the Detection and Classification of Tomato Leaf Diseases Using a Robust Deep Learning Framework

Tomatoes areAa noteworthy horticultural crop that has considerable importance in a diverse range of culinary traditions. At now, the primary concern for food security is in the realm of plant diseases. Researchers are actively working towards developing a streamlined approach to detect and diagnose illnesses in their early stages, with the ultimate goal of enhancing the agricultural industry. Currently, computer scientists and engineers are actively engaged in the fast development of a diverse range of tools and methodologies, with a special focus on the field of artificial intelligence. The advancement of cutting-edge machine learning applications for artificial intelligence relies on the establishment of original methods and frameworks. In contrast to the single-layer topologies of more traditional neural network learning methods, "deep learning" makes use of networks with many processing layers. In this research, a DL model is developed to detect & diagnose plant diseases by analyzing healthy & unhealthy plant image samples using deep learning techniques. The dataset contains 43,823 images of plants, including healthy plants and unhealthy plants. For this model implementation, follow some methodology processes like data preprocessing, image segmentation, data balancing, data splitting classification and detection, and assess the model's efficacy. The study uses a Fine-Tuned EfficientNetB7 approach with an impressive Mean Average Accuracy of 99.31%. AThe proposed technique demonstrates efficacy in early detection and has the potential for further improvement in terms of performance, hence facilitating the development of a real-world automated system for detecting plant diseases in agricultural settings.