Rice is a major crop in India. Near about 70%, the economy depends on agriculture products. However, Agriculture production is uncertain due to natural calamities, environmental conditions, and unpredictable plant diseases. Plant diseases are nearly impossible to recognize by the open eye by the farmers and crop producers. Therefore, an automatic detection system is a modern approach. Many automatic plant disease detection systems are developing by the researchers. In this paper, we present a summary of different image processing and machine learning techniques applied to the recognition of rice leaf and seedling diseases. Various attributes considered for doing a survey include segmentation type, segmentation techniques, features extracted, dataset size, author's name, and publication year, category of disease, techniques used, detection/classification accuracy, and future scope/limitations. We have gone through several research papers and a brief review of recent image processing and machine-learning techniques used by the researchers has been provided for the rice leaf and seedlings disease detection and classification.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.