Remote sensing inversion model and method for starch content of overground part of rice based on cross validation Lars regression algorithm

Abstract

The invention provides a remote sensing inversion model and method for the starch content of an overground part of the rice based on a cross validation Lars regression algorithm, which is a cross validation-based Lars regression model of Python language, and further provides model parameters of the cross validation-based Lars regression model. The invention further provides a remote sensing inversion method for the starch content of the overground part of the rice based on the cross validation Lars regression algorithm. The remote sensing inversion model for the starch content of the overground part of the rice based on the cross validation Lars regression algorithm can quickly and accurately obtain the starch content information of the overground part of the rice, and overcomes the difficulty that the characteristic wave band of the starch content of the overground part of the rice is difficult to determine due to the spectral superposition effect caused by complex components of the rice; a high-precision inversion model can be constructed from limited large sample data by using cross validation and a Lars regression algorithm, so that the precision of the inversion model for thestarch content of the overground part of the rice is improved.

Description

technical field [0001] The invention relates to the technical field of agricultural remote sensing, in particular to the technical field of measuring starch content in the aboveground part of rice, and specifically refers to a remote sensing inversion model and method for starch content in the aboveground part of rice based on a cross-validated Lars regression algorithm. Background technique [0002] The starch content in the aboveground part of rice refers to the weight percentage of starch in the aboveground part of rice. It is an important parameter to quantify carbon dioxide fixation and carbohydrate synthesis in rice photosynthesis. The influence of factors such as fertilizer and water. [0003] Monitoring the starch content in the aboveground part of rice, mastering the physiological conditions and growth conditions of rice photosynthetic products such as synthesis, transport, storage and accumulation, can not only ensure the yield and quality of rice production, but a...

AI Technical Summary

Problems solved by technology

In the process of constructing the inversion model of starch content in the aboveground part of rice, the spectral range measured by the full-band spectrometer covers 350nm to 2500nm. At the same time, the rapid processing of hyperspectral data has become an urgent technical problem to estimate the starch content of rice shoots based on hyperspectral data.

In addition, due to the heavy workload in the sample collection and testing process of starch content in the aboveground part of rice, it is difficult to obtain a large number of modeling samples, and the traditional modeling method is not conducive to building an inversion model suitable for popularization and application.

[0005] Therefore, it is hoped to provide a remote sensing inversion model for starch content in the aboveground part of rice, which can quickly and accurately obtain the starch content information in the aboveground part of rice, and overcome the characteristic bands of starch content in the aboveground part of rice caused by the spectral superposition effect caused by the complexity of rice components. It is difficult to determine the difficulty, and it is possible to construct a high-precision inversion model from limited large-sample data, and improve the accuracy of the inversion model for starch content in the aboveground part of rice.

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