A survey on detection and classification of rice plant diseases

Identifying disease from the images of the plant is one of the interesting research areas in computer and agriculture field. This paper presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of disease infected rice plants. This paper presents not only survey of various techniques but also concisely discusses important concepts of image processing and machine learning applied to plant disease detection and classification. We carry out detailed study of 19 papers, covering the work on rice plant diseases and other different plants and fruits, and present a survey of these papers based on important criteria. These criteria include size of image dataset, no. of classes(diseases), preprocessing, segmentation techniques, types of classifiers, accuracy of classifiers etc. We utilize our survey and study to propose and design our work on detection and classification of rice plant diseases.