Crustacean Shell Pattern Identification for Stable Chitin Quality
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Solution Overview
Problem
Current methods for identifying crustacean shells lack the ability to distinguish between different species and account for variations in shell quality due to factors like animal age, season, and composition, leading to poor and unstable chitin production.
Innovation Solution
A method and device that utilize a processor to analyze a digital image of a crustacean shell, identify unique shell patterns, and match them to stored patterns in a database to accurately identify the crustacean species, allowing for improved chitin production by distinguishing between different crustacean species and their molts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional methods are used to process crustacean shells for chitin production, then production volume can be maintained, but the quality of chitin is poor and unstable due to contamination from flesh particles and inability to distinguish species
Solution Approach 1:
The patent applies preliminary action by implementing species identification and shell quality assessment BEFORE the chitin production process. The system captures images of whole shells or shell fragments, analyzes them to determine species, age, and quality characteristics, and only then proceeds to processing. This preliminary classification ensures that only suitable shells are processed, preventing contamination issues and ensuring consistent chitin quality from the outset.
Solution Approach 2:
The patent replaces manual inspection and mechanical sorting methods with an automated optical imaging and image processing system. Instead of relying on physical examination of shells by hand or simple mechanical separators, the system uses digital cameras to capture images and computer algorithms to automatically identify species, estimate age, and assess shell quality. This substitution enables more precise and consistent quality control while reducing labor requirements.
2Stability of the object's composition
If shells from different species and ages are processed together, then production efficiency is maintained, but the resulting chitin has varying properties and poor quality stability
Solution Approach 1:
The patent applies segmentation by dividing the heterogeneous shell material into distinct categories based on species, age class, and quality characteristics. The image analysis system separates shells into different groups (e.g., juvenile vs. adult, different species, high quality vs. low quality) and enables selective processing of each segment. This segmentation ensures that shells with similar properties are processed together, maintaining consistent chitin composition within each batch while allowing different processing parameters for different segments.
Solution Approach 2:
The patent utilizes parameter changes by analyzing multiple characteristics of each shell (species identification, age estimation, shell condition, presence of contamination) and using these parameters to determine appropriate processing pathways. The system can adjust processing parameters based on the identified shell properties, ensuring optimal chitin extraction for each type of shell while maintaining overall production efficiency through automated classification.
3Measurement precision
If manual inspection of shell properties is performed, then some quality assessment is possible, but it is time-consuming and cannot accurately determine species or age
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical imaging and image processing system. Digital cameras capture high-resolution images of shells, and computer algorithms automatically analyze these images to identify species based on morphological features, estimate age from growth patterns and shell characteristics, and assess quality. This automated system provides much higher measurement precision and accuracy while reducing inspection time from minutes per shell to seconds, enabling rapid classification of large numbers of shells.
Data Source
AI summary
A method and identification device (115) for identification of a shell (110) of a crustacean (100), wherein the method comprises receiving, in a processor (130), a captured digital image (200) of at least a part of a shell (110) of the crustacean (100), identifying, in the processor (130), a shell pattern (154) on the shell (110) using the digital image (200), wherein the shell pattern (154) is unique for each individual crustacean and identifying, in the processor (130), the shell (110) of the crustacean (100) by positively matching the identified shell pattern (154) to one of a plurality of stored shell patterns (152), wherein each of the stored shell patterns (152) is associated to one previously identified crustacean (102)