Quality Assessment of Crops using Machine Learning Techniques

Disease in a crop leads to low productivity and in turn leads to huge loss to the farmers. Thus, detection of disease in early stage will be beneficial for farmer so that necessary actions can be taken. This paper discusses supervised machine learning techniques to detect the disease in the plant with the help of the image of the plant. The comparison between classifications techniques are made in order to select model with highest accuracy. The Quadratic SVM results with highest accuracy of 83.3%. This trained model is used for the detection of new disease image.