Soil nutrient model transfer method of different regions based on multi-algorithm recommendation

Abstract

The invention belongs to a model transfer method, and discloses a soil nutrient model transfer method of different regions based on multi-algorithm recommendation. The method comprises the following steps that (1) soil spectroscopic data of the different regions is acquired, and a main sample and an auxiliary sample are set; (2) a calibration set and a testing set of the main sample are divided, while a calibration model of the main sample is built based on partial least squares, and the model effect is evaluated; (3) a standard set and an unknown set of the auxiliary sample are divided; (4) the auxiliary sample is subjected to the spectroscopy pretreatment; (5) through multi-algorithm model transfer, a prediction result of the unknown set of the auxiliary sample is obtained; (6) through evaluation and analysis, a model transfer algorithm with the best effect is recommended. According to the method, solving the problem of soil nutrient content prediction in the different regions by application of a soil nutrient content model is achieved, the model prediction effect is guaranteed, while the chemical method measurement time of soil nutrients is reduced simultaneously, so that the cost is lowered, the manpower and material resources are saved, and the soil nutrient prediction is achieved in a fast and simple way.

Description

technical field [0001] The invention relates to a model transfer method, in particular to a method for transferring soil nutrient models between different regions based on multi-algorithm recommendation. 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-estab...

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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