Plant Lead Disease Detection Using Machine Learning

Abstract

Identifying plant diseases visually is a labor-intensive task, less accurate, and limited to specific areas. However, using an automatic detection technique requires less effort, saves time, and achieves higher accuracy. Common plant diseases include brown and yellow spots, early and late scorch, as well as fungal, viral, and bacterial infections. Image processing is used to measure the affected area and to detect color differences in the diseased regions. Therefore, image processing plays a vital role in the detection of plant diseases. Disease detection involves steps such as image acquisition, image preprocessing, image segmentation, feature extraction, and classification. This project focuses on methods for detecting plant diseases using images of their leaves.