Deepwater net cage fish feeding control method and system
By combining fish information, image denoising processing, water body thermal stratification model and feeding volume control model, the accuracy of feeding control in deep-water cage fish farming is solved, and the optimization of the breeding environment and the healthy and rapid growth of fish are achieved.
CN119672765AActive Publication Date: 2025-03-21GUANGDONG OCEAN UNIVERSITY
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Patent Information
- Application Number
- CN202510173633.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-18
- Publication Date
- 2025-03-21
- Estimated Expiration
- 2045-02-18
AI Technical Summary
Technical Problem
The feeding methods in existing deep-water cage fish farming are difficult to achieve precise control, resulting in deterioration of water quality, waste of feed and fish health problems.
Method used
By acquiring fish information, performing image denoising, constructing a water body thermal stratification model and feeding volume control model, a breeding visual report is generated to achieve accurate fish feeding control.
Benefits of technology
It has achieved refined management and intelligent decision-making of deep-water cage fish, optimized the breeding environment, improved bait utilization, promoted the healthy and rapid growth of fish, and reduced water quality pollution.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a deepwater net cage fish feeding control method and system, and the method comprises the steps: generating a cultured fish detail list in a target deepwater region according to the fish information, and carrying out the multi-round spiced salt denoising processing of the obtained image data of the target deepwater culture region, inputting the underwater light data into the water body thermal stratification model to obtain each deepwater hierarchy and short wave radiation data corresponding to each deepwater hierarchy; and analyzing and identifying the image data through an AI graph identification algorithm to obtain fish growth data, constructing a feeding amount control model according to a cultured fish detail list, the fish growth data and the short-wave radiation data, and generating a culture visualization report to realize feeding control of fishes in the target deepwater culture area. Fish growth and health are analyzed through image data, a feeding control model is constructed and optimized according to data such as the ammonia nitrogen content, the dissolved oxygen content and short wave radiation, feeding amount control is optimized, the fish growth speed is increased, the fish growth quality is improved, and long-term sustainable breeding is achieved.
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