Shrimp Peeling Vision System for Automated Quality Control
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Solution Overview
Problem
Manual adjustment of shrimp-peeling equipment is labor-intensive and requires experienced monitoring to optimize throughput and quality, which can be inconsistent due to factors like shrimp species, size, and equipment wear.
Innovation Solution
An automated shrimp-processing system that uses a vision system to capture digital images of peeled shrimp, count tail segments, and classify them into quality classes, enabling real-time adjustment of peeling machine operational settings to improve yield and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of peeling equipment is used, then operational flexibility can be maintained, but labor costs increase and consistency deteriorates
Solution Approach 1:
The system uses vision sensors to automatically detect shrimp quality characteristics and tail segments, with the processor autonomously adjusting peeler operational parameters based on real-time feedback, eliminating the need for manual monitoring and adjustment while maintaining adaptability to different shrimp batches
Solution Approach 2:
The system continuously monitors peeled shrimp quality through vision systems and uses this feedback to automatically adjust peeler settings, creating a closed-loop control system that maintains consistent quality without manual intervention and reduces labor costs
2Productivity
If automated vision systems are implemented, then labor costs are reduced and consistency is improved, but device complexity increases
Solution Approach 1:
The vision system serves multiple functions including quality assessment, tail segment counting, and operational parameter determination, while the processor handles both analysis and control tasks, allowing the system to perform multiple functions with a unified device architecture that manages complexity
3Productivity
If real-time quality monitoring is implemented, then yield is improved, but measurement precision requirements increase
Solution Approach 1:
The system divides the shrimp into distinct segments (head, body, tail segments) and counts them individually using vision technology, allowing precise measurement of quality characteristics that directly correlate with yield optimization while managing the complexity of accurate detection
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances the efficiency and consistency of shrimp peeling by automatically adjusting operational parameters based on real-time quality and yield analysis, improving production quality and reducing labor costs.
Implementation Method 1
A vision system captures a digital image of peeled shrimps on the conveyor
Data Source
AI summary
Methods and systems using a vision system to process shrimp. The vision system captures images of samples of shrimps. The processor produces a digital image of the shrimps in the samples. Shrimps exiting a peeler are imaged to determine the number of tail segments in each. The shrimps are classified by the number of intact segments, and quality, yield, and throughput computed from the classification results. The processor can control operational settings of the peeler based on the classification results. In a larger system including other shrimp-processing equipment besides the peeler, other points along the processing path can be imaged by camera or sensed by other sensors to determine processing quality and to make automatic operational adjustments to the equipment.