Tea variety and quality classification method based on deep learning Teformer
By applying deep learning Teaformer model and visible-near-infrared spectral data in tea quality detection, combined with PCA dimensionality reduction and multi-head attention mechanism, the subjectivity and inefficiency of traditional tea detection methods are solved, and the rapid and accurate evaluation of tea varieties and quality is achieved.
CN119691515APending Publication Date: 2025-03-25XIAMEN UNIV
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Patent Information
- Application Number
- CN202411830868.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-12
- Publication Date
- 2025-03-25
AI Technical Summary
Technical Problem
Traditional tea quality detection methods have subjectivity, inefficiency and complexity, making it difficult to achieve rapid and accurate tea variety and quality assessment.
Method used
The Teaformer model based on deep learning is adopted, combined with visible-near-infrared spectral data and PCA dimensionality reduction technology, and the classification evaluation of tea varieties and quality is performed through multi-head attention mechanism and feedforward neural network.
Benefits of technology
It achieves high accuracy and efficiency in tea varieties and quality classification, with an accuracy rate of 98.30% or above, solving the subjectivity and inefficiency problems in traditional methods.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a tea variety and quality classification method based on deep learning Teformer, and relates to the field of spectral analysis and the field of artificial intelligence algorithms. The construction of the classification method is based on a PCA dimension reduction method and a Transform neural network model, and quality evaluation is carried out on the tea leaves through a lossless, rapid and efficient means. By collecting visible-near infrared spectrum data of the tea leaves and performing feature extraction and classification through Teaformer, accurate classification and quality evaluation of the tea leaves are realized. Comprising the following steps: collecting visible-near infrared spectrum data of a certain number of tea samples, and preprocessing the data, including de-noising and standardizing; training the spectral data by using a Teammer model, and learning internal characteristics of the spectral data; unknown samples are predicted and classified through the model, and the quality of the tea leaves is evaluated; and in combination with actual application requirements, model parameters are optimized, so that the accuracy and stability of detection are improved.
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