Convolutional Neural Network-Based Phytopathological Diagnostic Framework for Precise Plant Disease Identification and Classification

Abstract

Plant leaf diseases have a major impact on agricultural productivity worldwide and threaten food security. To address this challenge, this paper presents a deep learning-driven technique for the automatic Identifying and categorizing plant leaf diseases using CNN-based models. The proposed model is trained on a diverse dataset comprising images of both healthy and diseased leaves. with Image normalization and augmentation strategies employed To optimize accuracy, robustness As well as generalization. The CNN automatically extracts discriminative features from input images, enabling accurate disease identification without the need for manual intervention. Results indicate that the proposed model successfully reaches a high level of accuracy is 98.71% and outperforms conventional approaches, while maintaining efficiency under varying environmental even under varying Under lighting and background noise variations.. Furthermore, The system is lightweight along with optimized for use On mobile and edge computing devices, enabling in real-time disease monitoring directly  On site. This makes the approach practical and scalable,Enabling farmers to identify diseases promptly, thereby reducing crop losses. and promote more sustainable agricultural practices.