Tea garden yield prediction method based on multi-modal information

Abstract

The invention discloses a tea garden yield prediction method based on multi-modal information, and the method comprises the steps: 1), obtaining or generating a tea garden data set, each sample containing a tea garden image, tea garden environment data and yield; 2) training a tea garden yield prediction model by using the data set; wherein the tea garden yield prediction model comprises a tea garden image feature learning module, an environment feature learning module, a feature fusion module and a full connection layer; the tea garden image feature learning module is used for acquiring image features of the tea garden from the tea garden image; the environment feature learning module is used for acquiring environment features F omega of the tea garden from the tea garden environment data; the feature fusion module fuses the image features and the environment features F [omega] to obtain a feature FC, and inputs the feature FC into a full-connection layer to predict the tea yield of the tea garden; and 3) for a tea garden to be predicted, inputting image data and environment data of the tea garden to the trained tea garden yield prediction model to obtain the tea yield of the tea garden to be predicted.

Description

technical field [0001] The invention belongs to the field of tea production, in particular to a method for predicting tea garden yield based on multimodal information. Used for forecasting and estimating the production of tea. Background technique [0002] At present, the demand for tea is increasing, and more and more people are engaged in tea production and processing. Accurate prediction of tea production has always been a problem to be solved. Experienced tea farmers can make a rough prediction based on experience, but it is difficult for more people to evaluate tea production. Accurately predicting tea output can provide a more appropriate planning basis for subsequent tea picking and production. Therefore, a more accurate and intelligent tea production forecasting technology can provide great convenience for tea processing and production. [0003] At present, there are very few studies on tea yield prediction, and researchers generally use statistical analysis metho...

AI Technical Summary

Problems solved by technology

These methods have solved the problem of tea yield prediction to a certain extent, but still do not make full use of the information of tea gardens and tea leaves, and cannot predict tea yield quickly, conveniently and accurately in actual use.

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