Predictive Spatial Feed Distribution for Crustacean Aquaculture
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Solution Overview
Problem
Existing methods for feeding crustaceans, such as shrimps, in aquaculture systems face inefficiencies due to the challenge of determining optimal positions and times for feed distribution, leading to waste and reduced feeding efficiency, as crustaceans move and disintegration times of feed pellets are not accurately accounted for.
Innovation Solution
A method and system for predicting future spatial distribution of crustaceans within an enclosed volume, allowing for precise determination of feed insert positions and amounts based on actual and predicted distributions, considering factors like crustacean activity, size, and mobility, using image analysis and machine learning to optimize feed distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If feed pellets are thrown into the tank at specific times, then crustaceans can be fed, but feed waste increases and feeding efficiency decreases due to unpredictable crustacean movement and disintegration timing
Solution Approach 1:
The system performs preliminary actions by predicting the future spatial distribution of crustaceans before feed insertion occurs. The method determines where crustaceans will be located at a future time point, then inserts feed at positions that will coincide with their predicted locations, rather than reacting to their current positions. This anticipatory approach ensures feed is delivered when and where crustaceans are actually present, eliminating waste from missed feeding opportunities.
Solution Approach 2:
The system implements feedback by continuously monitoring the actual spatial distribution of crustaceans, comparing it with predicted distributions, and using this information to optimize future feed insertion decisions. The method uses observed crustacean movement patterns and disintegration rates as feedback to refine predictions and improve subsequent feeding operations, creating a closed-loop control system that reduces waste and increases efficiency.
2Length of stationary object
If feed pellets disintegrate slowly to allow crustaceans to detect and reach them, then feed reach distance increases, but feed may disintegrate before crustaceans arrive
Solution Approach 1:
The system performs preliminary calculation of the optimal disintegration time required for feed pellets to reach the bottom and become detectable by crustaceans. By predicting where crustaceans will be at future time points, the method determines the precise disintegration rate needed so that feed becomes available exactly when crustaceans arrive, preventing both premature disintegration and delayed availability.
Solution Approach 2:
The system dynamically adjusts the disintegration time parameter of feed pellets based on predicted crustacean movement patterns and detection speeds. By changing this critical parameter according to real-time predictions, the method ensures feed pellets disintegrate at the optimal rate - neither too fast nor too slow - to match the predicted arrival time of crustaceans at feed locations.
Data Source
AI summary
A method is disclosed for determining a spatial feed insert distribution for feeding crustaceans that are present in a volume (4) at least partially enclosed by one or more barriers (6) for keeping the crustaceans (8) in the volume. The method comprises determining an actual spatial distribution of crustaceans within the volume. The method also comprises, based on the determined actual spatial distribution, predicting, for a future time, a future spatial distribution of crustaceans within the volume. The method also comprises determining, based on the future spatial distribution of crustaceans, a spatial feed insert distribution. The spatial feed distribution indicates one or more positions at a boundary of the volume and/or within the volume from which feed is to be inserted in the volume.