Soil nutrient model transfer method based on canonical correlation analysis and linear interpolation

Abstract

The invention belongs to a model transfer method, and discloses a soil nutrient model transfer method based on canonical correlation analysis and linear interpolation. The method comprises steps: 1) obtaining soil spectroscopic data in different regions, setting a main sample and slave samples; 2) dividing a main sample calibration set and a testing set, using a partial least squares method to establish a main sample calibration model, and evaluating a model effect of the main sample calibration model; 3) dividing a slave sample standard set and an unknown set; 4) performing spectrum pretreatment on the slave samples; 5) using canonical correlation analysis combined with linear interpolation (CCA-LI) algorithm to transfer the model, to obtain a prediction result of the unknown set of the slave samples. The method realizes to use a soil nutrient content model to solve problems of soil nutrient content prediction in different regions. The method ensures the prediction effect of the model, and reduces time for soil nutrient chemical method measurement, reduces cost, saves manpower and material resources, and rapidly and simply realizes prediction of soil nutrients.

Description

technical field [0001] The invention relates to a model transfer method, in particular to a soil nutrient model transfer method based on typical correlation analysis and linear interpolation. Background technique [0002] The spectrum contains a wealth of material information, and the spectral analysis technology has the advantages of non-destructive and fast, and has been widely used in agriculture, food, industry and other fields. Modeling with spectral data and related chemical values ​​can quickly realize the prediction of chemical values ​​of unknown samples, but this spectral model has certain limitations in predicting unknown samples, and can only predict unknown samples within a certain range. Different temperatures, different instruments, different measurement conditions, and spectra of samples in different regions will lead to inaccurate prediction results. To solve this problem, one method is to collect sample spectra and chemical values ​​to re-establish the mod...

AI Technical Summary

Problems solved by technology

The model transfer for samples between different regions is mainly to add new samples to the original model and re-establish the model. This method is not only time-consuming, but also the prediction effect of the revised model will be reduced, and it cannot achieve accurate accuracy for unknown samples between different regions. predict

At present, based on spectral technology, there is no mature algorithm for the model transfer of soil nutrients between different regions. Therefore, it is necessary to provide a fast and accurate method for soil nutrient model transfer between different regions.

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