Tomato Leaf Disease Detection and Remedy Suggestion Using Machine Learning

Abstract

Tomato plants have a high specificity of different diseases, which reduce crop yield and fruit quality, directly reducing agricultural productivity. Most current solutions are restricted to disease detection alone and do not provide an integrated system to detect plant diseases with suggestions for remedies. Moreover, these models are not user-friendly and do not represent environmental heterogeneity, therefore, their applicability is constrained for farmers in different environmental conditions. AgroAI is a machine learning-based algorithm that utilizes Convolutional Neural Networks (CNN) for crop disease detection and provides solutions based on disease severity. Using a curated dataset of tomato leaf images for diseases like Target Spot, Late Blight, and Bacterial Spot, the system processes images uploaded by farmers, classifying diseases. Data augmentation and pre processing techniques were applied to enhance model robustness, and a straightforward interface was developed to facilitate ease of use for farmers.