Submerged Fish Imaging for Accurate Sea Lice Detection
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Solution Overview
Problem
Current methods for monitoring external fish parasites like sea lice in aquaculture are manual, time-consuming, and prone to inaccuracies, leading to over-treatment or under-treatment, and are challenged by optical distortions and fish aversion to light sources in marine environments.
Innovation Solution
A system comprising a submerged camera and electronic image processing system that detects fish and sea lice by analyzing images, using a ranging detector for precise focus and reduced illumination to minimize fish disturbance, allowing natural behavior and enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting methods are used to monitor external fish parasites, then the process is simple to implement, but it is time-consuming and prone to inaccuracies
Solution Approach 1:
The patent replaces manual mechanical counting with an automated electronic image processing system that uses computers and software algorithms to detect, count, and monitor parasites on fish. This substitution eliminates human labor while providing more precise and consistent measurements, directly resolving the contradiction between accuracy and time consumption.
2Measurement precision
If strong illumination is used to improve image quality, then image clarity increases, but fish aversion to light sources increases causing behavioral changes
Solution Approach 1:
The system uses periodic or pulsed illumination rather than continuous strong lighting. The camera captures images at specific intervals with controlled light exposure, sufficient for image capture but minimized to avoid causing fish stress or behavioral changes, thus resolving the contradiction between image quality and fish welfare.
3Productivity
If automated image processing is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The system integrates multiple functions into a single platform: image capture, fish detection, parasite detection, counting, and monitoring all occur within one automated system. This multi-functionality increases productivity while managing complexity by consolidating operations rather than requiring separate systems for each task.
Data Source
AI summary
A method for external fish parasite monitoring in aquaculture, comprising the steps of: —submerging a camera in a sea pen comprising fish; —capturing images of the fish with the camera; and —identifying external fish parasite on the fish by analyzing the captured images, characterized by the steps of: —distinguishing between at least two different classes of external fish parasite which differ in the difficulty of recognizing the external fish parasite; —calculating quality metrics for each captured image, the quality metrics permitting to identify the classes of external fish parasite for which the quality of the image is sufficient for lice detection.