Segmented direct correction and slope/intercept correction combined soil nutrient model transfer method

Abstract

The invention belongs to a model transfer method, and discloses a segmented direct correction and slope / intercept correction combined soil nutrient model transfer method. The method comprises the following steps of: 1) obtaining soil spectrum data among different regions, and setting primary and secondary samples; 2) dividing a primary sample correction set and a primary sample verification set, establishing a primary sample correction model via a partial least squares method, and evaluating the model effect of the model; 3) dividing a secondary sample standard set and a secondary sample unknown set; 4) carrying out spectrum preprocessing on the secondary samples; and 5) carrying out model transfer by adoption of a segmented direct correction and slope / intercept correction (PDS-S / B) combined algorithm so as to obtain a prediction result of the secondary sample unknown set. According to the method provided by the invention, the difficulty of predicting soil nutrient contents among different regions is solved by using a soil nutrient content model, so that the time of measuring the soil nutrients via a chemical method is shortened while the prediction effect of the soil is ensured, the cost is reduced, the manpower and material resources are saved, and the soil nutrient prediction is rapidly and simply realized.

Description

technical field [0001] The invention relates to a model transfer method, in particular to a soil nutrient model transfer method for subsection direct correction and slope and intercept correction. 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 th...

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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