Plant Disease Detection using Image Processing with Machine Learning

Abstract

Crop diseases pose a severe challenge to food security, yet in many parts of the world it is still challenging to promptly diagnose them owing to a lack of necessary infrastructure. Image processing may be the most effective method to anticipate this issue and provide findings that are remarkably precise. Support vector machines and deep CNN were used in this research by improving training effectiveness and accuracy. In this study, Plant Disease Dataset is used which involves images of healthy and diseased plants collected by under some controlled conditions, SVM (support vector machine which used for feature extraction of images) and CNN (it will classify the dataset images into diseased plant or healthy plant) and further it'll be recommending the fertilizers according to the plant diseases.