Konovalov, Dmitry A., Saleh, Alzayat, Efremova, Dina B., Domingos, Jose A., and Jerry, Dean R. (2019) Automatic weight estimation of harvested fish from images. In: Proceedings of the International Conference on Digital Image Computing. pp. 308-314. From: DICTA 2019: International Conference on Digital Image Computing: Techniques and Applications, 2-4 December 2019, Perth, WA, Australia.

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Abstract

Approximately 2,500 weights and corresponding images of harvested Lates calcarifer (Asian seabass or barramundi) were collected at three different locations in Queensland, Australia. Two instances of the LinkNet-34 segmentation Convolutional Neural Network (CNN) were trained. The first one was trained on 200 manually segmented fish masks with excluded fins and tails. The second was trained on 100 whole-fish masks. The two CNNs were applied to the rest of the images and yielded automatically segmented masks. The one-factor and two-factor simple mathematical weight-from-area models were fitted on 1072 area-weight pairs from the first two locations, where area values were extracted from the automatically segmented masks. When applied to 1,400 test images (from the third location), the one-factor whole-fish mask model achieved the best mean absolute percentage error (MAPE), MAPE = 4.36%. Direct weight-from-image regression CNNs were also trained, where the no-fins based CNN performed best on the test images with MAPE = 4.28%.

Item ID:	61472
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-7281-3857-2
Related URLs:	https://researchonline.jcu.edu.au/68786/
Copyright Information:	© 2019 IEEE
Additional Information:	A version of this publication was included as Chapter 6 of the following PhD thesis: Saleh, Alzayat (2020) Developing deep learning methods for aquaculture applications. Masters (Research) thesis, James Cook University, which is available Open Access in ResearchOnline@JCU. Please see the Related URLs for access.
Funders:	Australian Research Council (ARC), Mainstream Aquaculture
Date Deposited:	22 Jan 2020 00:38
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 100%
SEO Codes:	83 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 8301 Fisheries - Aquaculture > 830102 Aquaculture Fin Fish (excl. Tuna) @ 100%
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