Web-based Plant Leaf Disease Detection and Prescription Module

Plant disease (PD) is a persistent issue for farmers that reduces both their income and food production. Even for subject matter specialists, it might be challenging to stop the disease from scattering to other portions of the plant once it has begun. Although it takes time and requires a close inspection of the sick region, experts may identify and diagnose the ailment. Smallholder farmers do not have simple access to these experts while constituting a major section of our nation. To identify the plant leaf disease and to evaluate its phases, a proper approach is thus required. Thanks to the present internet revolution and advancements in computer vision models, computer vision can now be used in agriculture. Recently, the Convolutional Neural Networks (CNN) are regarded as the most advanced method for classifying pictures and are capable of making a conclusive diagnosis. To diagnose the illness using a web-based application module created using deep learning techniques through the Transfer Learning approach is used in this research paper. A pre-trained method is built on photos of diverse plant leaves from the Plant Village dataset. Various efficiency metrics such as f1-score, Precision, accuracy, recall calculations and tracking are simulated to validate the efficiency of the suggested approach. The accuracy of ResNet50 with the Plant Village Dataset was around 97.5%, and it will provide information on the disease spread level on the scanned leaf. This study will improve farming practices for producing food and reduce early-stage crop loss.