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

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.