Enhancing milled rice qualitative classification with machine learning techniques using morphological features of binary images

ABSTRACT

Rice is a globally important agricultural crop, with extensive cultivation and consumption in Asia. In Thailand, it is a primary food crop and a crucial export commodity. However, ensuring the quality standards of Thai rice is challenging due to variations in grain mixtures, making conventional inspection methods laborious and time-consuming. Human judgment in visual inspection introduces the risk of discrepancies. To address this, a swift and accurate solution is needed for quality analysis and differentiation of rice grain categories. Image processing techniques and machine learning offer a promising approach for accurate rice grain classification and reducing human grading errors. In a recent study focused on jasmine rice (KDML 105) samples, images of rice grains were captured using a developed device. Morphological features related to shape and size were extracted through image processing. The Boruta algorithm was employed to select significant features, which were then used to train various machine learning classifiers. After training and validation, the random forest classifier demonstrated the highest performance and was chosen as the main classification model. It was then tested with a new dataset to evaluate its identification accuracy. The selected model successfully classified four categories of rice grains with an accuracy exceeding 99.00%. While research efforts have primarily focused on classifying rice varieties and detecting grain abnormalities, incorporating a combination of morphology, color, and texture features is essential for highly accurate predictions. However, when it comes to predicting rice grain types with distinct shapes and sizes, considering relevant morphological characteristics during the model development process is sufficient to achieve highly precise and accurate results.

KEYWORDS:

• Rice
• rice quality
• rice image processing
• rice classification
• machine learning model

Acknowledgments

This research was supported by the Fundamental Fund of Khon Kean University, under the National Science, Research and Innovation Fund (NSRF), and the authors gratefully acknowledge the Postharvest Technology Innovation Center, Science Research and Innovation Promotion and Utilization Division, Office of the Ministry of Higher Education, Thailand. The Agricultural Machinery and Postharvest Technology Center and Department of Agricultural Engineering, Khon Kaen University, Thailand, provided support for all the facilitation in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10942912.2023.2264533.

Additional information

Funding

This research was supported by the Fundamental Fund of Khon Kaen University, under the National Science, Research and Innovation Fund (NSRF).