Adaptive Feeding Appetite Forecasting in Aquaculture
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Solution Overview
Problem
Conventional appetite prediction systems in aquaculture are inaccurate due to uncertainties in detecting changes in fish appetite and are influenced by challenging underwater environments, leading to inefficiencies in feed management, increased costs, and potential health issues for fish.
Innovation Solution
A system that combines data from multiple sensor types, including underwater acoustic and image sensors, to generate an aggregated appetite score by adaptively weighting predictions from different feeding appetite forecast models based on environmental conditions, improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional appetite prediction systems are used in aquaculture, then feed management can be performed, but prediction accuracy is poor due to uncertainties in detecting changes in fish appetite and challenging underwater environments
Solution Approach 1:
The patent combines multiple sensor types (acoustic sensors, image sensors, environmental sensors) and multiple forecast models to create an integrated appetite prediction system. This merging of diverse data sources and analytical approaches compensates for the limitations of individual sensors in challenging underwater environments, thereby improving both measurement precision and prediction reliability.
Solution Approach 2:
The system introduces environmental sensors (temperature, salinity, dissolved oxygen) as intermediary variables that mediate between raw sensor data and appetite predictions. These intermediary measurements account for environmental influences on fish behavior, allowing the system to distinguish between appetite changes caused by environmental factors versus those caused by actual hunger signals, thus improving detection accuracy.
2Measurement precision
If multiple sensor types and forecast models are combined to improve prediction accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the complex prediction system into separate, specialized components: acoustic sensors for detecting feeding sounds, image sensors for observing surface behavior, environmental sensors for monitoring water conditions, and distinct forecast models for different types of appetite indicators. This segmentation allows each component to be optimized independently while maintaining overall system accuracy, managing complexity through modular design.
Solution Approach 2:
The system employs a unified data processing platform that handles multiple sensor types and forecast models through common algorithms and interfaces. This multi-functional approach allows the same computational infrastructure to process diverse data sources (acoustic, visual, environmental) and apply different forecast models, reducing overall system complexity despite the variety of input sources.
Data Source
AI summary
Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system.