LeafScan: Plant Leaf Disease Detection Using Convolutional Neural Networks (CNN)

Authors: Hardik Chaturvedi, Aditi Verma, Dr. Raj Kumar

Abstract: Developing countries like India have Agriculture as a major part of their economy. Diseases in crops can damage yields and lead to a reduction in farmer's earnings. Traditional crop disease diagnosis approaches are still laborious and require an expert knowledge. To overcome this issue; this research brings Leaf Scan: an intelligent web application-based Vegetable and Crop Disease detection using Convolutional Neural Network (CNN). The system detects and predicates diseases using images of affected leaves and also suggests the treatment and prevention methods. The system was developed using the Plant Doc dataset and deep learning Mobile Net V2 architecture. The web application brings multi-lingual interaction, disease prediction based on real-time images, scan history management, visual reports, treatment and prevention data, information based on the use case and adaptive browsing interfaces etc. The system has estimated a correctness of 86% on 27 disease classes as per experimental analysis. This project aims to showcase the role of Artificial Intelligence and Deep Learning in augmenting agricultural productivity and crop yield and minimizing crop losses and supporting Precision Farming Technologies.