Underwater Shrimp Starvation Recognition via Edge Detection
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Solution Overview
Problem
Current whiteleg shrimp culture in China lacks efficient methods for determining starvation extent, leading to unreasonable feeding practices, feed waste, water pollution, and decreased productivity due to reliance on experiential feeding techniques.
Innovation Solution
A recognition method and device utilizing underwater imaging with a camera, light sources, and a processor to analyze shrimp movement speed and bait quantity, segmenting images, extracting edges, recognizing shrimp heads and tails, and calculating movement speed and bait quantity to determine starvation extent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If feeding is based on personal experience rather than scientific observation, then feeding operations are simple to perform, but feeding rationality deteriorates leading to feed waste and water pollution
Solution Approach 1:
The system enables automatic monitoring and analysis of shrimp feeding behavior through underwater imaging and AI recognition, allowing the feeding system to self-adjust based on observed shrimp activity and starvation extent, eliminating the need for manual experience-based judgment
Solution Approach 2:
The patent replaces manual feeding decision-making with an automated image recognition system that uses computer vision and AI algorithms to analyze shrimp behavior, substituting human experience with mechanical/optical detection and computational analysis
2Productivity
If feeding amount is increased to ensure shrimp nutrition, then shrimp growth is improved, but oxygen consumption increases and water pollution worsens
Solution Approach 1:
The system continuously monitors shrimp feeding behavior through underwater imaging and provides real-time feedback on starvation extent and feeding status, enabling dynamic adjustment of feeding amounts to match actual shrimp needs, preventing overfeeding and its associated pollution problems
Solution Approach 2:
The patent changes the feeding control parameter from fixed experience-based amounts to dynamically adjusted amounts based on measured shrimp behavior parameters such as movement speed, bait quantity, and starvation extent derived from image analysis
3Object-affected harmful factors
If feeding is reduced to minimize waste, then water quality is improved, but shrimp nutrition may be insufficient affecting growth
Solution Approach 1:
The system provides real-time feedback on shrimp feeding status and starvation extent through image analysis, enabling precise adjustment of feeding amounts to maintain optimal water quality while ensuring adequate shrimp nutrition based on actual observed needs
Solution Approach 2:
The patent implements dynamic feeding adjustment where feeding amounts are continuously adapted based on real-time shrimp behavior observations, allowing the system to respond to changing shrimp needs while maintaining water quality
4Device complexity
If traditional experience-based feeding is used, then no additional equipment is needed, but feeding rationality and productivity are low
Solution Approach 1:
The patent employs a multi-functional integrated system where a single underwater imaging device performs multiple functions including shrimp behavior monitoring, starvation extent analysis, feeding effectiveness evaluation, and automated feeding control, reducing the need for multiple separate complex systems
Data Source
AI summary
It is provided a recognition device for analyzing a starvation extent of a whiteleg shrimp based on underwater imaging, including a bracket, a camera mounted on a top of the bracket, a plurality of light sources for illumination, and a processor connected with the camera. The processor receives a shrimp image collected by the camera, extracts an edge image of the shrimp after the collected shrimp image is preprocessed, and calculates a movement speed of the shrimp and a quantity of baits after a head and a tail are recognized, to recognize starvation extent of the shrimp. It is also provided a recognition method for analyzing starvation extent of the whiteleg shrimp based on underwater imaging. The present disclosure may guide feeders to feed and realize reasonable culture for the shrimp, by capturing images of the shrimp in time and determining starvation the extent of shrimp.