Review Paper on Systematic Study of Leaf Disease Detection Using Accurate and Efficient ML Technique.

Abstract

In recent years, plant diseases have posed significant threats to global food security and ecosystem stability. Timely detection and management of these diseases are imperative to mitigate their adverse effects. This paper introduces Foliage Guard, a novel smart plant leaf disease detector leveraging machine learning techniques for accurate and efficient disease identification. Leaves Guard employs state-of-the-art image processing algorithms to analyze leaf images captured using low-cost sensors or smartphones. The system utilizes a deep learning architecture trained on a diverse dataset of plant diseases to classify the health status of leaves accurately. Additionally, Foliage Guard incorporates real-time disease monitoring and alert mechanisms, enabling farmers and gardeners to take pro active measures against outbreaks. Through extensive experimentation and validation of various plant species, Foliage Guard demonstrates superior performance compared to existing approaches, with high accuracy and rapid processing times. The proposed system holds promise for revolutionizing plant disease management practices, offering a cost-effective and accessible solution for early disease detection and prevention in agriculture and horticulture sectors.