Tomato Plant Disease Detection Using Image Processing for Agriculture Application

This research paper outlines a new method for detecting diseases in tomato plants using advanced image processing techniques. The proposed approach involves several steps, including capturing high-quality digital images of tomato plants with a camera and processing the images to improve their visual features. Once the diseased regions have been identified, a set of features are extracted from them using texture analysis and color- based features. These features provide a unique signature that can be used to distinguish between different types of diseases, such as early blight, late blight, and bacterial spot, with high accuracy. The proposed method was evaluated using a large dataset of tomato plant images with different types of diseases, and the results demonstrate that it can accurately identify and classify the diseases with high precision. By enabling early detection and identification of plant diseases, this method can help farmers prevent crop losses, reduce the use of harmful pesticides, and promote sustainable agriculture practices.