Machine vision based soybean quality evaluation

Abdul Momin

doi:10.1016/j.compag.2017.06.023

Machine vision based soybean quality evaluation

2017-08-01

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AI

DOI：10.1016/j.compag.2017.06.023

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A novel proof of concept was developed targeted at the detection of Materials Other than Grain (MOGs) in soybean harvesting. Front lit and back lit images were acquired, and image processing algorithms were applied to detect various forms of MOG, also known as dockage fractions, such as split beans, contaminated beans, defect beans, and stem/pods. The HSI (hue, saturation and intensity) colour model was used to segment the image background and subsequently, dockage fractions were detected using median blurring, morphological operators, watershed transformation, and component labelling based on projected area and circularity. The algorithms successfully identified the dockage fractions with an accuracy of 96% for split beans, 75% for contaminated beans, and 98% for both defect beans and stem/pods. (C) 2017 Elsevier B.V. All rights reserved.

Keyword:

Foreign materials identification

Front lit

Backlit

Image processing

Machine vision based soybean quality evaluation

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