Autonomous Aquaculture Feeding System with Depth-Segmented Sensors
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Solution Overview
Problem
Current aquaculture feeding systems, particularly in inland facilities, face challenges in minimizing feed waste due to variations in water temperature and salinity, which affect fish appetite and growth, leading to inefficient feed distribution and environmental pollution.
Innovation Solution
An autonomous aquaculture fish feeding system that uses a floatable vessel equipped with temperature and salinity sensors to collect data from different depths and areas of the pond, processing this information to predict optimal feed intake through a mathematical model, thereby adjusting feed dosage dynamically based on environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed sensors are used for monitoring water parameters, then device complexity is reduced, but measurement precision deteriorates due to water stratification in inland ponds
Solution Approach 1:
The patent divides the water column into multiple depth segments (surface, mid-depth, bottom layers) and deploys sensors at each segment to capture vertical stratification. This segmentation allows the system to measure temperature, salinity, and dissolved oxygen at different depths independently, providing comprehensive data without requiring a single complex sensor system.
Solution Approach 2:
The patent transitions from single-point surface measurements to three-dimensional vertical profiling by adding the depth dimension. Autonomous vessels traverse the pond while sensors measure parameters at multiple predetermined depths, transforming the measurement space from two-dimensional (surface area) to three-dimensional (volume), thereby capturing water stratification effects.
2Device complexity
If reactive waste detection methods are used, then device complexity is minimized, but productivity deteriorates due to delayed feed adjustment
Solution Approach 1:
The patent implements preliminary action by using sensors to detect water parameters (temperature, salinity, dissolved oxygen) and fish behavior indicators (surface breaking, feeding activity) before waste occurs. The system predicts feed intake requirements in advance and adjusts feed dosage proactively, preventing waste accumulation rather than reacting to it after detection.
Solution Approach 2:
The patent establishes a closed-loop feedback system where sensors continuously monitor water parameters and fish behavior, the processor analyzes this data to predict feed intake, and the automatic feeder adjusts dosage accordingly. This real-time feedback loop enables dynamic feed management that responds to changing environmental conditions and fish needs, improving feed conversion efficiency.
3Adaptability or versatility
If manual control is used for feeding operations, then adaptability to environmental conditions is reduced, but ease of operation is improved
Solution Approach 1:
The patent implements self-service by enabling the feeding system to autonomously monitor water parameters, predict feed intake requirements, and adjust feed dosage without human intervention. The autonomous vessels independently navigate the pond, collect data, and the processor automatically generates feeding recommendations or controls the automatic feeder, making the system self-regulating and adaptive to environmental changes.
Solution Approach 2:
The patent replaces manual mechanical feeding operations with an automated electronic control system. Sensors, autonomous vessels, processors, and automatic feeders work together to substitute human judgment and manual feed distribution with electronic monitoring and automated dispensing, thereby improving adaptability while maintaining operational simplicity through centralized control.
Data Source
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AI summary
Autonomous aquaculture fish feeding system comprising an autonomous vessel (2, 71) having a plurality of temperature and salinity sensors (72) for two or more predetermined pond water depth ranges; an electronic data processor (1) configured for calculating a fish weight prediction model from collected temperature and salinity data from said temperature and salinity sensors (72) from each predetermined pond water depth range; wherein said fish weight prediction model is segmented into one or more prediction model segments, optionally for each of the predetermined pond water depth ranges. The electronic data processor (1) may be configured for calculating the fish weight prediction model, for each prediction model segment, by: accumulating weight gained into an accumulated total weight, wherein the weight gained is calculated from the energy used at each prediction period, calculating the energy used from the accumulated total feeding intake; accumulating feeding intake into accumulated total feeding intake, wherein the feeding intake is calculated from the total weight, a temperature factor and a salinity factor.