A method and system for nondestructive testing of quality of bird's nest based on multispectral imaging
By combining multispectral imaging technology and convolutional neural networks with ultraviolet, visible, and near-infrared light sources, a non-destructive testing system for bird's nest quality has been established, solving the problems of low efficiency and low accuracy in traditional methods and achieving efficient and accurate bird's nest quality testing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING RONGSHUTANG BIOTECHNOLOGY CO LTD
- Filing Date
- 2026-03-25
- Publication Date
- 2026-07-17
AI Technical Summary
Existing traditional methods for classifying and identifying bird's nests are inefficient and inaccurate, cannot achieve non-destructive testing, and rely on human experience, making it difficult to accurately distinguish between genuine and fake products and their quality.
Multispectral imaging technology combined with convolutional neural networks is used to image bird's nests using ultraviolet, visible, and near-infrared light sources, establishing a multispectral image database, and achieving non-destructive testing through model training.
It enables rapid, accurate, and non-destructive testing of bird's nests, improves the stability and consistency of testing, reduces the complexity of human operation, can identify subtle differences, and reduces long-term operating costs.
Smart Images
Figure CN122409531A_ABST