Computer Vision Based Smart Leaf Disease Detection System

Efficient and accurate identification of plant leaf diseases is crucial for modern agriculture, ensuring optimal crop yield and quality. The method uses colours in images and a technique called K-Means clustering to identify diseases early. It involves several steps: first, adjusting the images to gray scale and making them clearer. Then, it finds the outlines of the leaf and keeps only the important boundaries. By figuring out the average colours in these boundaries, it captures the different colour patterns in each area. The K-Means clustering groups similar colours, showing where diseases might be. Testing this on various leaf images with different diseases proves it works well in spotting areas affected by disease. Getting precise outlines is crucial for accuracy. In summary, this method of using colors and K-Means clustering looks promising for spotting plant diseases early. It's good at finding odd color patterns linked to diseases, helping stop diseases from spreading. If used in farming technology, it could change how we check plant health. This research suggests a different way to find diseases based on small color changes. By using advanced image techniques and grouping methods, it improves how we check plant health. Testing it with different methods showed a high 97.2% accuracy in finding and identifying plant diseases.