Method for predicting content of winter wheat grain protein based on coupling of spectral indexes and climatic factors, and method for establishing prediction model

Abstract

The invention discloses a method for predicting the content of winter wheat grain protein based on coupling of spectral indexes and climatic factors, and a method for establishing a prediction model, aiming at solving the technical problems of low accuracy and universality of a current model for predicting the content of winter wheat grain protein. The method for establishing the model comprises the following steps: acquiring information; determining a light temperature accumulation index; establishing a light temperature nitrogen index; determining a best spectrum parameter; establishing a prediction model; and checking the model. Through the method, the model for predicting the content of the winter wheat grain protein is high in accuracy and good in universality; the expansibility and the stability are improved; the method is capable of effectively predicting the change of the content of the winter wheat grain protein under different cultivation and management conditions.

Description

technical field [0001] The invention relates to the technical field of non-destructive prediction of agricultural vegetation growth information, in particular to a method for predicting protein content of winter wheat grains based on the coupling of spectral index and climatic factors and a method for constructing a prediction model. Background technique [0002] In the practice of wheat breeding, production, storage and processing, grain protein content is an important quality index of wheat and an important index of classified harvesting and storage. According to the survey, the quality standard rate of high-quality wheat stored in my country is currently low, and less than 40% of the country's second-class strong gluten wheat has reached the national level. Among them, factors such as production management models, cultivation measures, and ecological environment all have an important impact on the quality of wheat. Strengthening wheat quality prediction and forecasting wil...

AI Technical Summary

Problems solved by technology

Previous studies have shown that the spectral reflectance of a specific band is significantly positively correlated with leaf nitrogen content, and the protein content at maturity can be indirectly predicted by using the characteristic spectrum at the late growth stage of wheat. The important influence of the formation has obvious regional characteristics; the effect of climate factors in the ecological model of protein content forecasting is clear, but there are many parameters, and the physiological mechanism is not clear, and the growth basis in the middle growth period seriously affects the accuracy of the forecast results

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