Machine Learning Fish Filet Identification System
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Solution Overview
Problem
Current methods for identifying fish filets are expensive, slow, and inconvenient, particularly in distinguishing between species like Cod and Haddock, leading to widespread seafood mislabeling that affects economic, environmental, and health impacts.
Innovation Solution
A system utilizing machine learning algorithms to process images of fish filets, encoding them into feature vectors for predictive models to accurately identify the species, enabling rapid, cost-effective, and convenient identification through mobile devices without the need for physical sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional identification methods are used, then accuracy in distinguishing fish species can be achieved, but the process becomes expensive and slow
Solution Approach 1:
The patent replaces traditional mechanical and laboratory-based identification methods (DNA sequencing, expert visual inspection) with an automated image processing system using machine learning algorithms. The system captures images of fish filets and uses trained neural networks to automatically identify species, eliminating the need for time-consuming laboratory analysis while maintaining high accuracy.
Solution Approach 2:
The patent creates a digital copy (image) of the fish filet and processes this copy through machine learning models to identify species characteristics. Instead of physically analyzing the filet through complex laboratory procedures, the system works with visual information captured by cameras, enabling rapid identification without direct manipulation of the sample.
2Measurement precision
If traditional identification methods are used, then species can be distinguished, but the process becomes complex and inconvenient
Solution Approach 1:
The patent implements a self-service identification system where the machine learning model automatically performs species identification without requiring expert intervention. The system takes images and autonomously determines species identity, making the process as simple as capturing a photo and receiving results, thereby greatly improving ease of operation while maintaining accuracy.
Solution Approach 2:
The patent creates a universal identification system that can handle multiple fish species through a single platform. The machine learning model is trained on diverse datasets and can identify various species using the same image processing pipeline, making the system adaptable and convenient for different applications without requiring separate specialized procedures for each species.
3Measurement precision
If DNA testing is used for identification, then accurate species determination can be achieved, but the method is expensive and not accessible to consumers
Solution Approach 1:
The patent replaces expensive, complex DNA testing equipment with inexpensive imaging devices such as smartphone cameras. The system uses readily available, low-cost hardware to capture images and performs all analysis through software algorithms, eliminating the need for expensive laboratory equipment and making the technology accessible to consumers and small businesses.
Solution Approach 2:
The patent substitutes complex biochemical DNA testing procedures with computational image analysis. Instead of requiring laboratory equipment for DNA extraction, amplification, and sequencing, the system uses machine learning algorithms to analyze visual features of fish filets, dramatically simplifying the identification process while maintaining accuracy.
Data Source
AI summary
One disclosed method involves encoding an acquired image of a fish filet into a first feature vector consumable by at least one predictive model, processing, with the at least one predictive model, the first feature vector to identify a type of fish from which the fish filet was cut, and causing at least one device to output an indication of the type of fish identified by the at least one predictive model. Another disclosed method involves associating first images of fish filets with metadata indicative of one or more types of fish from which the fish filets were cut, using the first images and the metadata to train at least one predictive model to categorize second images of fish filets into the one or more types of fish, and providing the at least one predictive model to at least one device so as to nable the at least one device to output an indication of the one or more types of fish based acquired images of fish filets.