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A winter wheat leaf area index inversion method and system

A leaf area index and winter wheat technology, applied in the field of winter wheat growth research, can solve the problems of dependence on spectral information, low inversion accuracy, and no consideration of winter wheat height information, etc., to achieve high inversion accuracy

Active Publication Date: 2019-08-30
BEIJING NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are very few studies on the inversion of crop leaf area index based on UAV digital images, and when inverting LAI, the spectral information is mainly considered, and the height information of winter wheat is not considered, resulting in the inversion results only relying on spectral information. Inversion accuracy is low

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  • A winter wheat leaf area index inversion method and system
  • A winter wheat leaf area index inversion method and system
  • A winter wheat leaf area index inversion method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0085] figure 1 This is a method flow chart of the method for inversion of the leaf area index of winter wheat in Example 1 of the present invention.

[0086] see figure 1 , the winter wheat leaf area index inversion method includes:

[0087] Step 101: obtain the digital image collected by the drone;

[0088] Step 102: splicing the digital images to obtain corresponding orthophotos and point cloud data;

[0089] Step 103: Calculate the various vegetation indices constructed in the visible light band for the orthophoto;

[0090] Specifically include:

[0091] The green leaf vegetation index GLA (Green leaf algorithm) is calculated using the following formula:

[0092] GLA=(2*G-R-B) / (2*G+R+B)

[0093] The following formula is used to calculate the ExG (Excess green index):

[0094] ExG=2*G-R-B

[0095] The following formula is used to calculate the excess green and super red differential vegetation index ExGR (Excess green minus excess red index):

[0096] ExGR=ExG-1.4*...

Embodiment 2

[0120] This embodiment is described by taking the inversion of the leaf area index of winter wheat in Cang County, Cangzhou City, Hebei Province as an example.

[0121] The growth period of winter wheat in Cangxian County is from October to mid-June of the following year, which is divided into jointing stage, booting stage, flowering stage, grain filling stage, and milk maturity stage. The whole growth period of winter wheat was divided into 3 stages (see Table 1): booting stage (growth stage before booting stage), flowering stage (booting stage to before grain filling) and grain filling stage (growth stage after grain filling). The fertilization and irrigation conditions of winter wheat in the study area were basically the same in each growth period. According to the data collected in this experiment, the inversion of the leaf area index at booting stage of winter wheat was studied.

[0122] Table 1 Phenological history of winter wheat in the study area

[0123]

[0124]...

Embodiment 3

[0146] figure 2 This is the system structure diagram of the third winter wheat leaf area index inversion system according to the embodiment of the present invention.

[0147] see figure 2 , the winter wheat leaf area index retrieval system includes:

[0148] The image acquisition module 201 is used for acquiring the digital image collected by the drone;

[0149] The splicing module 202 is used for splicing the digital image to obtain corresponding orthophoto and point cloud data;

[0150] A vegetation index calculation module 203, configured to calculate various vegetation indices constructed in the visible light band for the orthophoto;

[0151] An index extraction module 204, configured to select any vegetation index whose correlation with the winter wheat leaf area index satisfies a preset condition from a plurality of the vegetation indices, to obtain a first inversion index;

[0152] a height inversion module 205, configured to invert the height of winter wheat acco...

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Abstract

The invention discloses a winter wheat leaf area index inversion method and system. The method includes steps: obtaining digital images acquired by an unmanned aerial vehicle; splicing the digital images to obtain a corresponding orthographic image and point cloud data; calculating various plantation indexes constructed by a visible light waveband on the orthographic image; screening any plantation index whose winter wheat leaf area index correlation satisfies a preset condition from the various plantation indexes, and obtaining a first inversion index; performing inversion on the height of winter wheat according to the point cloud data, and obtaining a second inversion index; performing binary inversion on the first inversion index and the second inversion index, and obtaining a regression model; and performing inversion on the winter wheat leaf area index by employing the regression model. According to the inversion method and system, compared with a simple regression linear model only employing the plantation index, the inversion result of the winter wheat leaf area index is better, and height information of the winter wheat is additionally provided so that the inversion precision of the winter wheat leaf area index in an elongation stage is improved.

Description

technical field [0001] The invention relates to the field of winter wheat growth research, in particular to a winter wheat leaf area index inversion method and system. Background technique [0002] Leaf Area Index LAI (Leaf Area Index, LAI) is a key physiological parameter, which is used in crop growth monitoring and ecological research, and plays an important role in the study of crop photosynthesis and biochemical processes. LAI has been widely used in agriculture, ecological environmental monitoring and other fields. However, there are few studies on the inversion of crop leaf area index based on UAV digital images, and the spectral information is mainly considered when inverting LAI, and the height information of winter wheat is not considered, resulting in the inversion results only relying on spectral information. The inversion accuracy is low. SUMMARY OF THE INVENTION [0003] The purpose of the present invention is to provide a winter wheat leaf area index invers...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01B11/28
Inventor 张锦水高铭阳潘耀忠段雅鸣张杜娟
Owner BEIJING NORMAL UNIVERSITY