Bharti,Ravi Kumar, Amit kumar Jaiswal

The rising need for enhanced agricultural efficiency, along with escalating environmental hurdles, highlights the pressing need for innovative approaches to detect and manage plant diseases. This study presents a machine learning-based system designed to identify and determine the cause of plant ailments. By harnessing advancements in “machine learning and artificial intelligence”, the system delivers a scalable and proficient solution for accurately detecting plant diseases in real-time. The paper delineates the architecture, components, and methodologies integrated into the system, alongside experimental findings showcasing its efficacy and potential impact on agricultural practices. Moreover, we provide insights into the comparative analysis of machine learning techniques, including “K-nearest neighbors (KNN)”, “Naive Bayesian” and “Random Forest”, within the framework of the system. Furthermore, we explore the forthcoming obstacles and potential future directions in the domain of machine learning-driven plant disease detection and diagnosis systems

https://doi.org/10.58357/jedcs.2024.v3.i3.13