CropVigil: Tomato Leaf Disease Detection Using Deep InfoMax Algorithm

Modern agricultural approaches classify and eradicate tomato-weakening pathogens using classification systems. To increase output and ensure farming's survival, these diseases must be appropriately diagnosed. The disease's multi-symptom nature, the need for vast amounts of annotated data, and real-time execution complicate this technique. Deep InfoMax algorithm (DIMA) improves disease classification with deep learning, this method retrieves lots of data by training a deep neural network on tomato leaves. The network correctly classifies tomato leaf images as disease kinds after training. This technology is versatile enough for disease diagnosis, crop management, and yield optimisation. Detecting and treating leaf diseases improves tomato productivity and health. The suggested method will be confirmed through simulation studies conducted on different images of tomato leaf diseases, the method will be validated in this way. The present study's overarching goal is to demonstrate how DIMA may dramatically improve agricultural disease management