Hybrid Deep Learning Approach for Automated Plant Disease Detection in Precision Agriculture

Plant pests and diseases pose a significant challenge to agricultural productivity and economic stability. Traditional detection methods, which rely on manual inspection by farmers or experts, are often time-consuming, expensive, and susceptible to human error. This paper introduces a novel automated plant disease detection system utilizing a hybrid machine learning (ML) approach based on deep convolutional neural networks (CNNs). Leveraging advancements in computer vision, our model demonstrates enhanced precision in plant protection and extends the application of computer vision to precision agriculture. The methodology encompasses image collection and database creation, with validation by agricultural experts, followed by the development and training of a deep CNN framework. The proposed system effectively distinguishes healthy leaves from diseased ones and separates leaves from their environmental background, achieving an overall accuracy of 96.77%. This solution offers a reliable, efficient, and scalable tool for plant disease recognition, catering to the needs of both amateur gardeners and professional agriculturists.