A remote sensing monitoring method for wheat powdery mildew with disease characteristic preprocessing function

A technology for monitoring wheat powdery mildew and remote sensing, which is applied in the field of remote sensing image processing, can solve the problems of poor monitoring accuracy and high redundancy of wheat disease characteristics, and achieve the effect of improving monitoring accuracy and strong robustness

Active Publication Date: 2021-01-05
ANHUI UNIVERSITY
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Problems solved by technology

[0007] The purpose of the present invention is to solve the defects of high redundancy of wheat disease characteristics and poor monitoring accuracy in the prior art, and provide a remote sensing monitoring method for wheat powdery mildew with disease characteristic preprocessing function to solve the above problems

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  • A remote sensing monitoring method for wheat powdery mildew with disease characteristic preprocessing function
  • A remote sensing monitoring method for wheat powdery mildew with disease characteristic preprocessing function
  • A remote sensing monitoring method for wheat powdery mildew with disease characteristic preprocessing function

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[0071] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0072] Such as figure 1 As shown, a remote sensing monitoring method for wheat powdery mildew with disease feature preprocessing function described in the present invention uses the relief method to operate efficiently, and by calculating the weight of image features, higher weights are given to features with strong classification ability . Considering that the relief algorithm does not consider the correlation between image features and cannot remove the redundancy between image features, the mRMR method is used to remove the redundancy between image features, and the minimum redundancy between features is obtained. And the subset of features that have the greatest correlation between the feature and the target. Through relief comb...

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Abstract

The invention relates to a remote sensing monitoring method for wheat powdery mildew with a disease feature preprocessing function, which solves the defects of high redundancy of wheat disease features and poor monitoring accuracy compared with the prior art. The invention comprises the following steps: acquisition and preprocessing of remote sensing data; extraction of characteristic variables; processing of characteristic variables; construction and optimization of a monitoring model for powdery mildew; The invention combines two feature selection technologies of relief and mRMR with a support vector machine optimized by a genetic method to form an effective remote sensing monitoring of powdery mildew at a regional scale.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing monitoring method for wheat powdery mildew with a disease characteristic preprocessing function. Background technique [0002] Wheat powdery mildew is one of the main diseases in the production process of wheat. It can occur throughout the growth period of wheat, causing serious reduction in yield and quality. After the damage, the yield reduction is generally between 5% and 10%, and it can reach 20% in severe cases. above. Timely and effective monitoring of the occurrence of wheat powdery mildew is of great significance to improve the yield and quality of wheat. [0003] Although the traditional ground survey method has good survey results, it needs a lot of manpower and material resources and is not suitable for large-scale research. Many scholars use meteorological data to monitor and predict crop diseases and insect pests. Wang Heju...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/188G06F18/24143G06F18/29
Inventor 黄林生阮超黄文江张东彦赵晋陵翁士状曾玮丁文娟丁串龙
Owner ANHUI UNIVERSITY
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