Tomato Leaf Disease Detection using Image Processing

As the country’s economy majorly depends on agricultural productivity. The main cause of lower vegetable and fruit yield is plant disease. Annual agricultural Pest and disease-related losses are estimated to fifty thousand crore rupees, according to a survey by India's linked chambers of commerce and industry, which is substantial in a country where at least two hundred million Indians go to bed hungry [5]. Hence, a plant's worth is enormous. As a result, diagnosing plant diseases is essential for decreasing food waste and increasing the agriculture product with good quality and large quantity. Traditional methods to detect the plant illness are effective, but they must be carried out manually by analyzing plant leaf patterns and identifying the disease. However, it must be done by a person, and this must be done manually by studying plant leaf patterns and locating the plant's disease region. This takes more time, and many labors must be put to watch and monitor a large farm. Many agriculture centers and technologies have emerged in recent years to improve agricultural production. Plants are an important source of energy, but they are also prone to disease, which can result in social and economic losses. Many diseases are initially visible on plants, but if not detected early on, they can result in a significant loss. So, in order to save the time spent manually observing the plant process and locating it, this research study has devised a novel strategy by using Image Processing (IP) technique, which will be extremely effective in detecting plant illnesses. It goes through several processes to detect the disease, including image acquisition, image pre-processing, image segmentation, feature extraction, and classification. Histogram of an Oriented Gradient is used for extracting the features of an Image. The obtained images will be readily used to determine the diseased part of a leaf.