Method for estimating leaf SPAD (soil and plant analyzer development) values of winter wheat after regreening and before earing

A winter wheat and leaf technology is applied in the field of estimating the SPAD value of winter wheat leaves after turning green to before heading, which can solve problems such as expensive equipment and achieve the effect of improving accuracy and estimation accuracy.

Inactive Publication Date: 2017-05-31
QINGDAO AGRI UNIV
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However, the equipment required for hyperspectral technology is

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[0017] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and not to limit the present invention.

[0018] See figure 1 , This specific implementation adopts the following technical solutions:

[0019] A method for estimating the SPAD value of winter wheat leaves after turning green to before heading, the steps are as follows:

[0020] 1. Canopy image collection and analysis: digital image collection of winter wheat canopy to obtain field canopy image color parameters, the canopy image color parameters are hue (H), saturation (S) and lightness (V);

[0021] 2. Determination of SPAD value of field leaves: After collecting digital images of winter wheat canopy, in the digital image collect

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Abstract

The invention discloses a method for estimating leaf SPAD (soil and plant analyzer development) values of winter wheat after regreening and before earing. The method comprises steps as follows: (1), canopy image collection and analysis: winter wheat canopy is subjected to digital image collection, and canopy image color parameters are obtained; (2), SPAD value determination of field leaves: after collection of the digital images of the winter wheat canopy, SPAD values of unfolding leaves at the topmost part are determined by an SPAD-502 chlorophyll meter in a digital image collection area, and an average value is calculated; (3), estimating model construction: an SPAD value estimating model is constructed with an integrated neural network method according to the SPAD values of the winter wheat leaves and the corresponding canopy image color parameter data; (4), estimating model verification: the estimating model is verified with correlation coefficients, root-mean-square errors and relative root-mean-square errors. The leaf SPAD value estimating model based on winter wheat canopy image analysis is constructed with the integrated neural network method, so that accuracy of SPAD value monitoring of the winter wheat leaves is effectively improved.

Description

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Application Information

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Owner QINGDAO AGRI UNIV
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