Non-Invasive Juvenile Fish Trait Identification via Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current aquaculture methods face challenges in efficiently increasing fish production while managing disease prevalence, growth inefficiencies, and waste management, particularly due to the difficulty in tracking and sorting juvenile fish traits before they reach maturity, which can lead to increased disease risk and environmental impact.
Innovation Solution
A non-invasive system using trained artificial neural networks to identify genetic traits and biomarkers in juvenile fish based on external image characteristics, allowing for sorting by gender and disease resistance at an early stage, even when the fish are only 2-4 grams, thereby increasing farming efficiency and reducing environmental impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive genetic tests are used to determine gender and identify biomarkers in juvenile fish, then trait identification accuracy is improved, but specimen viability deteriorates (death of specimen)
Solution Approach 1:
The patent replaces invasive mechanical/genetic testing procedures with non-invasive optical imaging and machine learning analysis. External phenotype images captured by imaging devices are processed by trained neural networks to predict genotype biomarkers, eliminating the need for physical tissue sampling that kills the specimen while maintaining high identification accuracy
Solution Approach 2:
The patent introduces an intermediary system consisting of imaging devices and machine learning models that mediate between the need for trait identification and specimen preservation. The imaging system captures external characteristics, and the trained neural network translates these phenotypic observations into genotype predictions, serving as a non-invasive intermediary that eliminates direct contact with the specimen
2Ease of manufacture
If fish sorting is delayed until fish mature to over 50 grams, then conventional ultrasound technology can be used to determine traits, but farming efficiency deteriorates
Solution Approach 1:
The patent performs trait identification and sorting actions preliminarily at the juvenile stage (2-4 grams) rather than waiting for maturity. By training machine learning models to predict genotype biomarkers from external images of young fish, the system enables early sorting decisions that accelerate farming cycles and improve productivity without requiring advanced ultrasound technology
3Productivity
If the number of fish in aquaculture is increased, then production output is improved, but disease prevalence worsens
Solution Approach 1:
The patent applies local quality by identifying and sorting individual fish with superior disease resistance traits based on their phenotypic characteristics. By using machine learning to predict genotype biomarkers associated with disease resistance from external images, the system enables selective breeding and stocking of hardier individuals, thereby improving overall herd health and reducing disease prevalence in high-density aquaculture operations
4Measurement precision
If invasive genetic tests are performed on juvenile fish, then biomarker identification is improved, but operational complexity worsens
Solution Approach 1:
The patent replaces complex invasive genetic testing procedures with simpler non-invasive optical imaging and computational analysis. External phenotype images are captured using standard imaging devices and processed by trained machine learning models to predict genotype biomarkers, eliminating the need for complex laboratory procedures while maintaining accurate biomarker identification
Data Source
AI summary
Methods and systems are disclosed for improvements in aquaculture that allow for increasing the number and harvesting efficiency of fish in an aquaculture setting by identifying and predicting internal conditions of the juvenile fish based on external characteristics that are imaged through non-invasive means.