Comparative Study of Geometric Morphometrics and Machine Learning for On-Demand Rice Grain Grading

DOI

10.2991/978-94-6239-616-6_29How to use a DOI?

Keywords

Morphometric analysis; Image processing; Computer vision; Machine Learning

Abstract

Grain quality plays an integral role to agricultural productivity and global food security, influencing market price, customer preference and trade regulation. Rice grains had been traditionally categorized in ‘full’ and ‘broken’ by manual visual inspection which is laborious, time consuming subjective process leading to some errors. However, this traditional procedure needs the demands of modern agricultural supply chains, especially for entities such as FCI, CMR, rice millers, etc… Where accuracy and efficiency are imperative. This paper proposes a novel morphometric image processing method for detection & classification of rice grains. The classification algorithm implemented is rudimentary, yet powerful as it constructs its own “gold standard” by recognizing the largest grain present in every analyzed micrograph to serve as a reference for ‘full’ grains, all remaining grains are classified according to either geometric properties. In the framework two main types of approaches are compared: (i) geometric properties and (ii) Machine learning used such as Convolutional Neural Networks (CNN), Random Forest, Support Vector Machines (SVM) and XGBoost to improve classification accuracy and applicability. This inexpensive, reliable and scalable alternative could be a solution to modernize the quality control in rice processing industry and provide justice to farmers with price premium for their products.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Nimmagadda Vishnu Datta
AU  - Sriharinadha Savaram
AU  - R. Vaisshale
PY  - 2026
DA  - 2026/03/31
TI  - Comparative Study of Geometric Morphometrics and Machine Learning for On-Demand Rice Grain Grading
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 364
EP  - 374
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_29
DO  - 10.2991/978-94-6239-616-6_29
ID  - Datta2026
ER  -