Tea variety identification method and system based on deep neural network

Abstract

The invention discloses a tea variety identification method and system based on a deep neural network. The method comprises the following steps: collecting tea sample data to obtain a first data set; preprocessing the first data set to obtain a second data set; respectively constructing three prediction models of linear regression, random forest and deep neural network by adopting the second data set; training the linear regression prediction model, the random forest prediction model and the deep neural network prediction model, and selecting the prediction model with the highest accuracy; optimizing the prediction model with the highest accuracy to form a final prediction model; and inputting the second data set into the final prediction model to obtain a classification result. According to the method, the three models are adopted to predict the tea variety data set, the prediction model with the highest accuracy is selected through training and comparison, and important parameters of the prediction model are optimized, so that the final model is formed, and more reliable prediction precision is obtained.

Description

technical field [0001] The present invention relates to the technical field of biological information identification, and more specifically, relates to a tea variety identification method and system based on a deep neural network. Background technique [0002] The quality of tea is particularly related to the variety and origin of raw materials. However, the current domestic tea market has insufficient effective identification of quality and serious information asymmetry, which has weakened the advantage of the origin of tea varieties in my country. Therefore, it is very necessary to develop an effective and accurate tea variety identification method, which has direct practical significance for maintaining tea brand and improving tea quality. Contents of the invention [0003] The primary purpose of the present invention is to provide a method for identifying tea varieties based on a deep neural network in order to solve the problems existing in the prior art, optimize the...

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Problems solved by technology

[0002] The quality of tea is particularly related to the variety and origin of raw materials. However, the current domestic tea market has insufficient effective identification of quality and serious information asymmetry, which has weakened the advantages of the origin of tea varieties in my country.

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