Effluent Pellet Tracking for Precise Fish Farm Feed Control
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Solution Overview
Problem
Current automated feeding systems in land-based fish farms lack precision, leading to inefficiencies such as overfeeding or underfeeding, which result in wasted resources, increased costs, and environmental stress due to inaccurate monitoring of uneaten fish feed.
Innovation Solution
An AI-based system utilizing a feed camera and object enhancer to capture and analyze images of uneaten feed pellets, processing images to determine pellet trajectories, and generating a real-time pellet count to adjust feeding quantities accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If automated feeding systems use rough estimates of pellet waste (pellet intensity or cross-section measurement), then device complexity is reduced, but measurement precision deteriorates leading to substantial waste or starvation
Solution Approach 1:
The patent replaces mechanical measurement methods (pellet intensity, cross-section measurement) with an optical system consisting of a camera and image processing algorithm. The camera captures images of pellets passing through the effluent pipe, and computer vision algorithms automatically count and measure them, providing precise data without complex mechanical sensors.
Solution Approach 2:
The patent creates an optical copy (image) of the physical pellets in the effluent pipe. By capturing images and processing them computationally, the system generates accurate measurements of pellet waste without physically interfering with the flow or requiring direct contact with the pellets, thus maintaining precision while keeping the system relatively simple.
2Ease of operation
If operators use qualitative monitoring of waste as a baseline, then ease of operation is improved, but measurement precision deteriorates resulting in overfeeding or underfeeding
Solution Approach 1:
The system enables self-service automated feeding by using image data to automatically calculate pellet waste and adjust feeding rates without operator intervention. The camera continuously monitors the effluent pipe, and the control system automatically adjusts the feeding mechanism based on real-time waste measurements, eliminating the need for operators to manually assess waste levels.
Solution Approach 2:
The patent implements a closed-loop feedback system where the camera monitors pellet waste in real-time, the control system processes this data to determine actual consumption, and the feeding mechanism automatically adjusts future feed delivery based on this feedback. This continuous feedback loop ensures precise feeding control without requiring operator expertise or manual measurement.
3Measurement precision
If operators physically count wasted feed pellets caught in filters, then measurement precision is improved, but loss of time and productivity worsen due to being discrete and time-consuming
Solution Approach 1:
The patent transforms the discrete, periodic manual counting process into a continuous automated monitoring system. The camera continuously captures images of pellets passing through the effluent pipe in real-time, and the control system continuously processes this data to provide ongoing measurements of pellet waste, eliminating dead time between measurements and enabling continuous feeding optimization.
Solution Approach 2:
The patent replaces the manual mechanical counting process with an optical measurement system. Instead of operators physically collecting and counting pellets from filters, the camera optically detects and counts pellets as they pass through the effluent pipe, and the control system automatically processes this data, eliminating the time-consuming manual labor while maintaining or improving counting accuracy.
Data Source
AI summary
An AI-based system and method for feed monitoring in a land-based fish farm, where the fish farm has a tank containing fish and an effluent pipe that is coupled to the tank where uneaten feed pellets and non-pellet objects flow through the effluent pipe to an exit port. A feed camera is mounted to an effluent pipe for capturing a video feed having images of objects that traverse a field of view of the feed camera when each image is acquired. A special-purpose computer executes a pellet-tracking algorithm that employs a region of interest (ROI) proposal module, an ROI classification module, an ROI tracking module, and a trajectory classification module for at least counting uneaten feed pellets. The computer generates in real time a pellet count based on the pellet trajectories.