Method for building winter wheat full-rot disease index prediction models on basis of spectral indexes and application

Abstract

The invention discloses a method for building winter wheat full-rot disease index prediction models on the basis of spectral indexes, and aims to provide an effective, simple and convenient method forpredicting winter wheat full-rot disease indexes. The method includes investigating winter wheat disease situations in sampling points; comparing the differences of hyper-spectral information of winter wheat canopies under the different disease indexes conditions on the basis of acquired filling-stage winter wheat canopy-ground ASD (analytical spectral device) spectral data and UHD185 (ultrahigh-definition 185) imaging hyper-spectral data; screening winter wheat full-rot sensitive wave band ranges and spectral indexes in UHD185 spectral data by the aid of the ASD spectral data; building linear regression models of the spectral indexes and winter wheat full-rot disease indexes; predicting winter wheat full-rot disease situations. The invention further discloses a method of predicting the winter wheat full-rot disease situations by the aid of the models. The methods have the advantages that the methods are high in work efficiency, prediction results are high in precision, and technicalguidance can be provided for preventing incidence and development of full-rot wheat.

Description

technical field [0001] The invention relates to crop disease monitoring technology, in particular to a construction method and application of a spectral index-based winter wheat take-all disease index prediction model. Background technique [0002] Wheat take-all occurs in major wheat producing countries at home and abroad. Wheat is the main food crop in Henan Province. The first occurrence of take-all occurred in Yuanyang, Junxian and Fugou counties in 1992. The entire growth stage from the seedling stage to the mature stage of wheat can be infected by the pathogen of take-all disease, and the infection at the seedling stage will cause the wheat plants to be short, and the roots and stem bases will turn gray-black; in a humid environment, the jointing stage In the root and stem bases of diseased plants at the filling stage, there will be a gray-black mycelial layer visible to the naked eye, commonly known as "black feet". "Black feet" and "white spikes" are typical symptoms...

AI Technical Summary

Problems solved by technology

[0003] At present, the monitoring and forecasting methods for pests and diseases in my country are still visual inspection and field sampling. Although this traditional manual inspection method has good authenticity and high accuracy, it is time-consuming and labor-intensive, and it cannot be inspected on a large scale. , leading to omissions and misjudgments in non-monitored areas or areas with atypical characteristics, resulting in the continued spread of pests and diseases

In addition, the information acquisition and feedback in the traditional way also has a lag, which seriously affects the accuracy and timeliness of data

[0004] At present, the use of spectral prediction technology to obtain field crop growth information is very mature, but there is a lack of application on the level of fine spectral feature information for winter wheat disease monitoring, and there are very few researches on this technology for wheat take-all disease.

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