In recent years, Artificial I ntelligence, and more specifically Visual Artificial Intelligence (Visual AI), has emerged as a transformative tool in aquaculture. W hile its application has been explored in fish farming , its potential in shrimp aquaculture is now only beginning to be realized . This study presents a case study on the implementation of visual AI technology developed by INVE Aquaculture in collaboration with ARIS BV , aiming at enhancing the monitoring and management of marine shrimp larviculture .
The SnappArt 360 system, initially introduced for counting Artemia and rotifers utilizes deep learning-based object detection networks, trained on images sourced globally and annotated by experts to accurately identify shrimp across all larval stages , from nauplius to postlarvae. A standardized sampling protocol was established to gather representative samples from transport bags, pooled tanks, and culture tanks.
The key outcome of this study was the ability to track shrimp larval development, indicated by the Larval Development Index (LDI), enabling rapid and accurate tracking of larval shrimp development. The AI system demonstrated high accuracy in detecting developmental stages and quantifying population dynamics, providing insights that traditionally required expert analysis or were previously inaccessible .
This technology allows data-driven decision-making in feed management by offering real-time stage-specific information thereby optimizing feed allocati on and reducing feed waste , which remains one of the most significant costs in aquaculture. Furthermore, the system includes reports for real-time monitoring and historical data retrieval, supporting performance analysis and culture benchmarking.
In conclusion, the integration of visual AI into marine larval shrimp represents a significant advancement in precision feeding, offering expert insights that enhance operational efficiency.