Authors:
Yousif Hashisho
1
;
Tim Dolereit
1
;
Alexandra Segelken-Voigt
2
;
Ralf Bochert
2
and
Matthias Vahl
1
Affiliations:
1
Fraunhofer Institute for Computer Graphics Research IGD, Joachim-Jungius-Str. 11, 18059 Rostock, Germany
;
2
Institute of Fisheries, State Research Centre of Agriculture and Fisheries Mecklenburg-Vorpommern, Südstraße 8, 18375 Born, Germany
Keyword(s):
Computer Vision, Image Processing, AI, Deep Learning, Shrimp, Aquaculture.
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.