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Quality inspection of fertilizer granules using computer vision – a review
I.K. Ndukwe 1, D.V. Yunovidov 2, M.R. Bahrami 1,4, M. Mazzara 1, T.O. Olugbade 3

1 Innopolis University,
University Street 1, Innopolis, Tatarstan, 420500, Russia;
2 Logic Yield,
Gvardeyskaya Street 14, Kazan, Tatarstan, 420073, Russia;
3 University of Dundee,
Dundee, Scotland, DD1 4HN, United Kingdom;
4 Samarkand International University of Technology,
Samarkand 140100, Uzbekistan

 PDF, 1179 kB

DOI: 10.18287/2412-6179-CO-1458

Pages: 84-94.

Full text of article: English language.

Abstract:
This research explores the fusion of computer vision and agricultural quality control. It investigates the efficacy of computer vision algorithms, particularly in image classification and object detection, for non-destructive assessment. These algorithms offer objective, rapid, and error-resistant analysis compared to human inspection.
     The study provides an extensive overview of using computer vision to evaluate grain and fertilizer granule quality, highlighting granule size's significance. It assesses prevailing object detection methods, outlining their advantages and drawbacks.
     The paper identifies the prevailing trend of framing quality inspection as an image classification challenge and suggests future research directions. These involve exploring object detection, image segmentation, or hybrid models to enhance fertilizer granule quality assessment.

Keywords:
Quality control, computer vision, machine vision, machine learning, grains, fertilizer granules.

Citation:
Ndukwe IK, Yunovidov D, Bahrami MR, Mazzara M, Olugbade TO. Quality inspection of fertilizer granules using computer vision - a review. Computer Optics 2025; 49(1): 84-94. DOI: 10.18287/2412-6179-CO-1458.

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