Utilizing Machine Learning Techniques for Plant-Leaf Diseases Classification

Abstract

Infectious diseases of plants provide a substantial danger to the world's food supply and the agricultural industry. The early detection and classification of diseases that affect plant leaves is essential for minimizing crop loss and creating disease management measures that are both efficient and effective. For the purpose of this research, we present a unique approach that utilizes advanced machine learning techniques in order to classify plant diseases in a manner that is both accurate and efficient. In the first part of this multi-part series, we will begin by providing a detailed analysis of several different machine learning techniques, such as deep learning, convolutional neural networks (CNNs), and K-nearest neighbor (KNN), support vector machines (SVMs). Next, we provide an overview of a methodology for preprocessing the leaf images, which includes the addition of enhancements to the images, segmentation of the images, and the extraction of features. Next, we apply various machine learning algorithms to a large, diverse dataset of plant-leaf images that have varying degrees of disease severity and compare the performance of these algorithms as they are implemented on the dataset. Our findings provide evidence that the method being proposed is successful in correctly recognizing and categorizing plant diseases that affect leaf tissue. In terms of accuracy, precision, and recall, the models that are based on deep learning, in particular CNNs, perform significantly better than classical machine learning techniques. In addition, we investigate various methods to enhance the interpretability of the model and provide insights into the primary factors that contribute to the accuracy of categorization.