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Winter wheat late freezing disaster monitoring and yield prediction method based on Internet of Things and remote sensing inversion

A late frost damage and remote sensing inversion technology, applied in the field of agricultural engineering, can solve the problem of not considering the impact of winter wheat late frost damage

Active Publication Date: 2019-10-08
HENAN AGRICULTURAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved in the present invention is to provide a method for monitoring and yield forecasting of winter wheat late frost injury based on the Internet of Things and remote sensing inversion, so as to solve the technical problem that the existing method does not consider the impact of winter wheat late frost injury and directly predicts the yield

Method used

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  • Winter wheat late freezing disaster monitoring and yield prediction method based on Internet of Things and remote sensing inversion
  • Winter wheat late freezing disaster monitoring and yield prediction method based on Internet of Things and remote sensing inversion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0067] Example 1: Model building based on sum of growing degree days (SGDD) and sum of extreme degree days (SEDD)

[0068] The sum of GDD and EDD of winter wheat from the date of sowing to harvest of winter wheat is respectively SGDD and SEDD.

[0069] The sowing date of the entire Henan Province is uniformly selected as October 15 each year, and the harvest date is set as June 1 each year. Establish a regression model with winter wheat yield as the dependent variable and SGDD and SEDD in the growth process of winter wheat as independent variables. The formula is as follows:

[0070] - formula (I);

[0071] In the formula, Y is the yield of winter wheat, SGDD and SEDD are the sum of growing degree days and the sum of extreme degree days respectively; β 0 is the intercept of the equation; β G , β E are the influence degrees of SGDD and SEDD on winter wheat yield respectively, correspondingly expressing the change of winter wheat yield when SGDD and SEDD change.

[0072] ...

Embodiment 2

[0084] Example 2: Multivariate-based model construction

[0085]The yield of winter wheat during the growth process is the result of the combined effects of many factors. Among them, the normalized difference vegetation index (NDVI) value can directly reflect the photosynthesis and growth of winter wheat, and is closely related to the yield of winter wheat. NDVI is used as an influencing factor of the model. The value of NDVI used in constructing the model is the peak value of NDVI at the heading stage of winter wheat in April.

[0086] The winter wheat yield is predicted based on multiple variables, and a regression model is established between the winter wheat yield and SGDD, SEDD, and NDVI. The formula is as follows:

[0087] - Formula (V);

[0088] In the formula, Y is the yield of winter wheat, β 0 is the intercept of the equation; β G , β E are the effects of SGDD and SEDD on winter wheat yield, respectively, β N Represents the degree of influence of NDVI on winte...

Embodiment 3

[0099] Example 3: Improvements to Multivariate Models

[0100] The normalized difference vegetation index (NDVI) value during the growth process of winter wheat can directly reflect the photosynthesis and growth of winter wheat. The growth of winter wheat is relatively lush from April to early May. At this time, the growth of winter wheat has a stronger correlation with the yield of winter wheat. The NDVI value of week, 17 week and 19 week was used as the influencing factor of the model.

[0101] Model in embodiment 2 is improved, winter wheat yield is predicted, winter wheat yield and SGDD, SEDD and the NDVI of each period are established regression model, and formula is as follows:

[0102] - Formula (VII);

[0103] In the formula, Y is the yield per unit area of ​​winter wheat, SGDD and SEDD are the sum of growing degree days and the sum of extreme degree days respectively; NDVI 1 is the average value of the 14th week NDVI calculated by formula (VI) in Example 2; NDVI ...

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Abstract

The invention discloses a winter wheat later freezing disaster monitoring and yield prediction method based on Internet of Things and remote sensing inversion, and aims to solve the technical problemthat the yield prediction is directly carried out without considering the influence of winter wheat later freezing disaster. The invention designs a winter wheat later freezing disaster monitoring method based on Internet of Things and remote sensing inversion and a construction method of a winter wheat yield prediction model, and provides a winter wheat yield prediction method. According to the invention, the occurrence of the winter wheat later freezing disaster can be accurately and accurately monitored, the extreme climate is quantified, the decision guidance and suggestions are provided for the timely disaster detection and post-disaster remedy; the influence of the extreme climate on the winter wheat yield is reduced, a yield estimation model is further perfected, and an Internet ofThings and remote sensing data fusion method is provided for the yield prediction; a new idea is provided for the intelligent decision diagnosis of the winter wheat production in Hebei Province, the guidance and suggestions are provided for the agricultural production decision, and the data support is provided for the wheat production and the market transaction.

Description

technical field [0001] The invention relates to the technical field of agricultural engineering, in particular to a method for monitoring late frost damage and yield prediction of winter wheat based on the Internet of Things and remote sensing inversion. Background technique [0002] Winter wheat is the main food crop in my country and is closely related to my country's food security. Henan Province is one of the core areas of grain production in my country. The planting area of ​​winter wheat accounts for more than 70% of the cultivated land area, and the output of winter wheat in Henan Province accounts for more than 25% of the total wheat output in the country. The stable and high yield of winter wheat in Henan Province is crucial to ensuring national food security. important. [0003] In recent years, the occurrence of extreme climates in the world tends to be frequent, and the frequency of late frost damage of winter wheat in Henan Province has increased, and the degree...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/02G06F17/18
CPCG06Q10/04G06Q50/02G06F17/18
Inventor 时雷张娟娟马新明许鑫宋利红秦雅倩段其国
Owner HENAN AGRICULTURAL UNIVERSITY
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