UPM Institutional Repository

Optical imaging for food grain quality evaluation–recent advances and future perspectives


Citation

Ageh, Opeyemi Micheal and Hashim, Norhashila and Shamsudin, Rosnah and Che Man, Hasfalina and Mohd Ali, Maimunah and Onwude, Daniel I. and Dasore, Abhishek (2025) Optical imaging for food grain quality evaluation–recent advances and future perspectives. International Journal of Agricultural Sustainability, 23 (1). art. no. 2569166. pp. 1-26. ISSN 1473-5903; eISSN: 1747-762X

Abstract

Optical imaging techniques have gained widespread popularity in grain quality evaluation due to their non-destructive nature, rapid analysis, and ability to provide detailed information about various grain properties. This review highlights recent advancements in optical imaging technologies, including hyperspectral imaging (HSI), multispectral imaging (MSI), RGB imaging, fluorescence imaging (FI), thermal imaging (TI), and ultraviolet imaging (UVI) along with their future perspectives. These techniques are discussed in terms of their principles, applications, and potential for non-destructive assessment of grain quality parameters such as composition, defects, and contamination. A comparative analysis of these techniques is provided, emphasizing their precision, application areas, and limitations. Challenges such as calibration model development, illumination variability, and the complexity of image analysis are critical for widespread adoption to construct robust and generalised models. Despite these challenges, integrating these imaging techniques offers significant opportunities for improving grain sorting, storage, and processing. By providing comprehensive and objective data, these technologies have the potential to revolutionize grain quality monitoring and enhance postharvest management practices, enabling greater efficiency and reduced costs.


Download File

[img] Text
123228.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (2MB)

Additional Metadata

Item Type: Article
Subject: Agronomy and Crop Science
Subject: Economics and Econometrics
Divisions: Faculty of Engineering
Smart Farming Technology Research Centre
DOI Number: https://doi.org/10.1080/14735903.2025.2569166
Publisher: Taylor and Francis Ltd.
Keywords: Artificial intelligence; Food grain; Grain quality evaluation; Hyperspectral imaging; Non-destructive testing; Optical imaging
Sustainable Development Goals (SDGs): SDG 2: Zero Hunger, SDG 12: Responsible Consumption and Production, SDG 9: Industry, Innovation and Infrastructure
Depositing User: Ms. Siti Radziah Mohamed@mahmod
Date Deposited: 28 Apr 2026 00:50
Last Modified: 28 Apr 2026 00:50
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/14735903.2025.2569166
URI: http://psasir.upm.edu.my/id/eprint/123228
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item