Effective and efficient automatic detection, prediction and prescription of potential disease in berry family

Abstract

The grape cultivation industry in India faces significant challenges from fungal pests and diseases, leading to substantial economic losses. Detecting leaf diseases in grape plants at an early stage is crucial to prevent infections from spreading, minimize crop damage, and apply timely and precise treatments. This proactive approach is vital for maintaining the productivity and quality of grape cultivation. Integrated technology is crucial for improving grape production and minimizing the use of harmful pesticides. Developing smart robots and computer vision-enabled systems can efficiently detect and predict diseases, reducing human labor and optimizing grape production. The CNN algorithm achieved an accuracy of 98% using the real-time dataset, making it a highly effective method for image training and classification. VGG16 and Improved VGG16 achieved accuracies of 95% and 96%, respectively, indicating their strong performance. MobileNet and Improved MobileNet achieved accuracies of 86% and 97%, respectively. Utilizing Convolutional Neural Networks (CNN) for grape plant leaf detection facilitates precise and automated differentiation between healthy and diseased leaves by analyzing their visual features. This method not only enables early disease detection but also calculates the total area of the leaf affected by the disease. Such an approach presents a promising solution to enhance productivity in grape cultivation.