Mr.Chekuri Mahesh., Aditi Kumari

Abstract

In the realm of precision agriculture, the accurate identification of phytopathological conditions in plant foliage is crucial for effective disease management and crop yield optimization. This paper delineates the development of an advanced application designed to identify and classify phytopathological conditions in foliage through the implementation of a Convolutional Neural Network (CNN) architecture. The proposed system is embedded within a comprehensive project framework that integrates various components to enhance its functionality and efficiency. The CNN architecture, selected for its efficacy in image analysis tasks, is meticulously engineered to process and analyze high-resolution images of plant leaves. This architecture is trained on a diverse dataset comprising annotated images of different plant diseases, enabling it to discern subtle variations and patterns indicative of specific pathological conditions.