AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production

Yousif Hashisho, Tim Dolereit, Alexandra Segelken-Voigt, Ralf Bochert, Matthias Vahl

Abstract

Shrimp farming is a century-old practice in aquaculture production. In the past years, some improvements of the traditional farming methods have been made, however, it still involves mostly intensive manual work, which makes traditional farming a neither time nor cost efficient production process. Therefore, a continuous monitoring approach is required for increasing the efficiency of shrimp farming. This paper proposes a pipeline for automated shrimp monitoring using deep learning and image processing methods. The automated monitoring includes length estimation, assessment of the shrimp’s digestive tract and counting. Furthermore, a mobile system is designed for monitoring shrimp in various breeding tanks. This study shows promising results and unfolds the potential of artificial intelligence in automating shrimp monitoring.

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Paper Citation


in Harvard Style

Hashisho Y., Dolereit T., Segelken-Voigt A., Bochert R. and Vahl M. (2021). AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 710-716. DOI: 10.5220/0010342007100716


in Bibtex Style

@conference{visapp21,
author={Yousif Hashisho and Tim Dolereit and Alexandra Segelken-Voigt and Ralf Bochert and Matthias Vahl},
title={AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={710-716},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010342007100716},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production
SN - 978-989-758-488-6
AU - Hashisho Y.
AU - Dolereit T.
AU - Segelken-Voigt A.
AU - Bochert R.
AU - Vahl M.
PY - 2021
SP - 710
EP - 716
DO - 10.5220/0010342007100716