Plant Leaf Disease Identification using Machine Learning

Abstract

Agriculture is very important in India since it is a growing nation. Nearly six-in-ten individuals living in rural areas of India rely on farming for their livelihood. As one of the world's most popular produce items, tomatoes play a vital role in many people's daily meals. Therefore, identifying and classifying any diseases a tomato plant may have is essential for preventing substantial loss in tomato quantity and production. Such problems are addressed using cutting-edge tech by employing a broad range of approaches and techniques, such as image processing. As with many other plants, a tomato plant's leaves are the first to exhibit signs of a disease. Four steps were used in the research to narrow down the potential illness types. There are four steps total: data cleansing/preprocessing, leaf segmentation, feature extraction, and classification. First, we utilise picture preprocessing to get rid of any distracting backgrounds, and then we use image segmentation to single out the areas of the leaf that took the brunt of the impact. It is possible to employ the supervised, complex machine learning method known as a Convolutional Neural Network (CNN) to find solutions to classification and regression issues. If the user has reached this stage, they should seek help. Diseases have the most devastating impact on plant life. This research demonstrates how image processing may be used to detect flaws in tomato plants by examining images of the affected leaves.