Transforming Agriculture with Deep Learning Approaches to Plant Health Monitoring

Abstract

Effective management of plant diseases is crucial for ensuring agricultural productivity and sustainability. This research presents a novel framework for intelligent plant disease detection leveraging deep learning techniques. The proposed framework integrates multiple stages to facilitate accurate and efficient diagnosis through deep Convolutional Neural Networks (CNNs). Initially, high-resolution images of plant leaves are acquired using smartphones or cameras, followed by preprocessing steps such as resizing and normalization to prepare the data for analysis. A deep CNN architecture extracts intricate features from the preprocessed images, enabling precise disease classification. Post-processing stages provide users with diagnostic outputs and relevant information, enhancing decision-making in agricultural management. Continuous model retraining with updated datasets ensures adaptability to new diseases and environmental conditions. Experimental results demonstrate the framework's effectiveness in achieving high accuracy and robust performance across various plant species and diseases. This research contributes to advancing the application of AI in agriculture, offering a scalable solution for proactive plant health monitoring and sustainable farming practices, while also informing social sciences in planning and development by promoting food security and rural development.