Analysis of SVM and KNN in the detection of leaf diseases

By utilizing the proposed automated strategy we can recognize the diseases without disappointment so we can lessen the use of manures by distinguishing the diseases. And along these lines we can lessen the wastage of cash and human exertion. The accuracy of the framework can be expanded by expanding the quantity of information base pictures. Here we got the greatest accuracy. It tends to be improved by expanding the pictures for every disease. Our efforts were focused on creating an automated method that could be used in agriculture to identify, count, categorize, and inspect mature or immature leaves. Over Leaf Diseases, the applied methodology was successfully implemented, and a satisfactory outcome was noted.