Application of Image Analysis in Food Grain Quality Inspection and Evaluation During Bulk Storage
Click here to download PDF


Image analysis
machine vision
computer vision
grain quality evaluation
bulk storage

How to Cite

Aviara, N. A., Adesanya, A. A., Iyilade, I. J., Olorunsola, E. O., & Oyeniyi, S. K. (2022). Application of Image Analysis in Food Grain Quality Inspection and Evaluation During Bulk Storage. ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT, 18(4), 693-706. Retrieved from


With increased expectations for high quality food products and safety standards, the need for accurate, fast and objective quality determination of moisture, purity, germination and pathogen free food products and grains during bulk storage continues to grow. This paper reviews the application of image analysis in food grain quality inspection and discusses the potential of the technology for application in grain quality monitoring and evaluation during bulk storage. Image analysis procedure and physical properties of the grain bulk were also discussed. Image analysis is an automated alternative to manual inspection with less processing time and more accurate results. Human inspection has been found wanting due the bias judgment and results. Image analysis of food grains during bulk storage can only get better with its diverse applications in varietal identification, distinctness, uniformity and stability (DUS) testing, detection of insects and foreign bodies within the grain bulk and the detection of hot spot in the bin to mention a few. In Nigeria, image analysis will be a great tool in terms of grain quality preservation in National Grain Reserves (NGRs), providing seeds for farmers for the next planting season and reviving the grain reserves in Nigeria to boost gainful employment of citizens. An automated grain reserves using image analysis is possible in Nigeria only if appropriate government policies and funding can throw its full weight behind the innovation.


Click here to download PDF
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2022 Arid Zone Journal of Engineering, Technology and Environment published by University of Maiduguri, Nigeria