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Quality inspection of fertilizer granules using computer vision – a review

Ikechi Kalu NdukweInnopolis University , University Street 1 , Innopolis , Tatarstan , 420500 , Russia;D. V. YunovidovLogicYield , Gvardeyskaya Street 14 , Kazan , Tatarstan , 420073 , Russia;Logic YieldMohammad Reza BahramiInnopolis University , University Street 1 , Innopolis , Tatarstan , 420500 , Russia;Manuel MazzaraInnopolis University , University Street 1 , Innopolis , Tatarstan , 420500 , Russia;Temitope Olumide OlugbadeUniversity of Dundee , Dundee , Scotland , DD1 4HN , United Kingdom;

2025en

Ochiq kirishOlmos

Litsenziya: cc-by

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.18287/2412-6179-co-1458

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