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

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Abstract

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.

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Acknowledgements

The authors would like to thank the National Institute of Technology, Raipur for providing a research facility and giving the lots of encouragement for the application based data analysis for the mankind.

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    Bharati Patel & Aakanksha Sharaff

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Conceived and Designed the Analysis: Online dataset has been taken for the analysis of rice crop variety detection and spikletes counting by research scholar Bharati Patel (Corresponding Author).

Collected the Data: According to requirement of experimental analysis data has been taken by the authors Dr. Aakanksha Sharaff and Bharati Patel through the online sources and as a future we are trying to apply for the local data taking from IGKV, Raipur (In progress).

Contributed Data or Analysis Tools: Working on image processing tools like MATLAB, python etc. by research scholar Bharati Patel.

Performed the Analysis: Analysis is to be done by Dr. Aakanksha Sharaff and Bharati Patel.

Paper Writing: Paper work has been completed by the research scholar Bharati Patel with the consent of Dr. Aakanksha Sharaff.

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Correspondence to Bharati Patel.

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Patel, B., Sharaff, A. A review on rice plant phenotyping traits estimation for disease and growth management using modern ML techniques. Multimed Tools Appl 83, 37771–37793 (2024). https://doi.org/10.1007/s11042-023-17098-8

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