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Deep learning based automatic grading of bi-colored apples using multispectral images

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Abstract

Grading of apple fruits involves their inspection, assessment and sorting by quality. Machine vision has been the industry’s choice as it is fast, reliable and tireless. Recently deep learning has brought revolutionary advances in computer vision and machine learning. Accordingly this study presents a deep learning based quality grading solution for apple fruits. A 2D convolutional neural network is trained on multispectral images of bi-colored apples to realize two-category and multi-category grading. For the multi-category grading, the performance of a novel cascaded CNNs based solution is further investigated. Experimental results show that the proposed deep learning solution achieves highly accurate and fast grading performance outperforming the state-of-the-art.

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Acknowledgements

This work is funded by the General Directorate of Technology, Research and Energy of the Walloon Region of Belgium with Convention No 9813783.

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Correspondence to Devrim Unay.

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Unay, D. Deep learning based automatic grading of bi-colored apples using multispectral images. Multimed Tools Appl 81, 38237–38252 (2022). https://doi.org/10.1007/s11042-022-12230-6

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