Smartphone Based Grape Leaf Disease Diagnosis and Remedial System Assisted with Explanations

Plant diseases are one of the biggest challenges faced by the agricultural sector due to the damage and economic losses in crops. Despite the importance, crop disease diagnosis is challenging because of the limited-resources farmers have. Subsequently, the early diagnosis of plant diseases results in considerable improvement in product quality. The aim of the proposed work is to design an ML-powered mobile-based system to diagnose and provide an explanation based remedy for the diseases in grape leaves using image processing and explainable artificial intelligence. The proposed system will employ the computer vision empowered with Machine Learning (ML) for plant disease recognition and explains the predictions while providing remedy for it. The developed system uses Convolutional Neural networks (CNN) as an underlying machine/deep learning engine for classifying the top disease categories and Contextual Importance and Utility (CIU) for localizing the disease areas based on prediction. The user interface is developed as an IOS mobile app, allowing farmers to capture a photo of the infected grape leaves. The system has been evaluated using various performance metrics such as classification accuracy and processing time by comparing with different state-of-the-art algorithms. The proposed system is highly compatible with the Apple ecosystem by developing IOS app with high prediction and response time. The proposed system will act as a prototype for the plant disease detector robotic system.