A review on rice plant phenotyping traits estimation for disease and growth management using modern ML techniques

Over the past decades, rice crops have been crucially acknowledged as one of the most powerful energy streams for the production of resources. Plant phenotyping trait estimation includes the external feature evaluation of the plants for production growth. Phenotyping using machine learning techniques outperforms the other imaging techniques for the analysis of traits including leaf, seed, branch, panicle, flower root, shoot, etc. Rice plants, categorized by multiple traits such as growth analysis and disease management, are considered a contributing factor to the agricultural, economic, and communal losses in the upcoming development of the agricultural field. The last 15 years’ diagnosis of plant disease in relation to image processing techniques has remained an area of interest among researchers. Several disease detections, identification, and quantification methods have been developed and applied to a wide variety of crops. This paper reviews the related research papers from the period between 2007 and 2023, with a focus on the development of the state of the art. The related studies are compared based on image segmentation, feature extraction, feature selection, and classification. This paper also outlines the current achievements, limitations, and suggestions for future research associated with the diagnosis of rice plant growth analysis and disease identification using machine learning techniques.