Deep learning applied in fish reproduction for counting larvae in images captured by smartphone

Costa, Celso Soares; Zanoni, Vanda Alice Garcia; Curvo, Lucimar Rodrigues Vieira; Carvalho, Mário de Araújo; Boscolo, Wilson Rogério; Signor, Altevir; Arruda, Mauro dos Santos de; Nucci, Higor Henrique Picoli; Marcato, José; Gonçalves, Wesley Nunes; Diemer, Odair; Pistori, Hemerson

doi:10.1016/j.aquaeng.2022.102225

Cited by 11 publications

(1 citation statement)

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“…The study in [20] deployed a dual frequency identification sonar to store and count fish in a draft tube. The work of [21] performed 140 experiments using Faster R-CNN R50-FPN 2X and Grid R-CNN-X101-32X4d-FPN 2X to count tilapia larvae based on 301 images and 6.195 larvae in the fish reproduction phase reaching a mean Average Precision (mAP) 0.5 of 97.30 %. The study in [22] presented a YOLO-based fish detection method using Euclidean tracking to count fish in a pond in fish farms.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Counting Breeding Eels Using Mathematical Morphology Operations and Boundary Detection

Tran

Chau

Tran

et al. 2022

Applied Computer Systems

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The Mekong Delta region of Vietnam has great potential for agricultural development thanks to natural incentives. Many livestock industries have developed for a long time and play an important role in the country with many agricultural export products. In the era of breakthrough technologies and advances in information technology, many techniques are used to support the development of smart agriculture. In particular, computer vision techniques are widely applied to help farmers save a lot of labour and cost. This study presents an approach for counting eels based on Mathematical Morphology Operations and Boundary Detection from images of breeding eels captured with the proposed photo box. The proposed method is evaluated using data collected directly from a breeding eel farm in Vietnam. The authors of the research evaluate and investigate the length distribution of eels to select the appropriate size for counting tasks. The experiments show positive results with an average Mean Absolute Error of 2.2 over a tray of more than 17 eels. The contribution of the research is to provide tools to support farmers in eel farms to save time and effort and improve efficiency.

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Section: Related Workmentioning

confidence: 99%