LEAF DISEASE PREDICTION

Abstract

Leaf diseases are a major problem in agriculture, causing significant losses in crop yield and quality. Early detection of leaf diseases is essential for effective management, but it can be difficult and time- consuming to do manually. In recent years, there has been growing interest in the use of machine learning and computer vision techniques for leaf disease prediction. These techniques can be used to automatically extract features from leaf images that are indicative of disease, and then use these features to train a classifier that can distinguish between healthy and diseased leaves. Several studies have shown that machine learning-based methods can achieve high accuracy in leaf disease prediction. For example, one study reported an accuracy of 98% for detecting 10 different types of leaf diseases in tomato plants. The development of accurate and reliable leaf disease prediction methods has the potential to revolutionize the way that plant diseases are managed. By enabling early detection of diseases, these methods can help to reduce crop losses and improve crop yields Keywords CNN, Image processing, Convolution operations, Fully connected layer, Machine learning, Computer vision