An Extensive Evaluation of Plant Disease Detection Using Diverse Machine Learning Approaches

Plant sicknesses fundamentally affect horticultural efficiency and food security. In order to minimize widespread damage and maximize crop yield, it is essential to detect these diseases promptly and accurately. Automating the process of detecting plant diseases has been shown to be a promising application of machine learning techniques in recent years. This study presents a comprehensive analysis of various machine learning techniques applied to the task of plant disease detection. This study investigates the use of different AI procedures for plant illness recognition. Convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and k-nearest neighbors (KNNs) are just a few of the algorithms that are compared in this study. Feature extraction from diseased plant images is the first step in our experiment, followed by model training and validation. The results demonstrate the efficacy of machine learning in accurately identifying plant diseases based on visual symptoms. The findings provide insights into the strengths and limitations of each technique, paving the way for further research in automated and precise plant disease diagnosis to enhance agricultural sustainability.