Plant Leaf Disease Identification Method Using Computer Vision and Machine Learning Algorithms

This paper proposes an image processing-based method for detecting plant diseases. The system's three key phases are acquisition, pre-processing, and disease identification. Pictures of plant leaves are taken during the image acquisition stage using a digital camera. The image preprocessing stage involves enhancing and pre-processing the collected images to eliminate noise and extraneous data. The pre-processed photos are finally sorted into healthy or unhealthy, utilizing machine learning techniques in the illness identification step. This paper will represent how we can detect plant leaf diseases using a specific software system. It will also find the percentage of accuracy and error of disease detection based on the number of detected images. The leaf disease finding procedure information will also be explained here step by step. According to experimental data, the proposed approach has a high level of accuracy in detecting plant illnesses. Farmers can utilize the system as a valuable tool to detect plant illnesses early, minimizing yield losses and enhancing crop management techniques.