Hyperspectral citrus leaf disease identification method based on characteristic wavelength

A characteristic wavelength and disease identification technology, applied in the field of hyperspectral citrus leaf disease identification, can solve the problems of high cost and cumbersome molecular biological detection operations, and achieve accurate identification, good discrimination effect and simple calculation

Active Publication Date: 2020-02-07
ZHONGKAI UNIV OF AGRI & ENG
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and proposes a hyperspectral citrus leaf disease identification method based on characteristic wavelengths, which can realize 4 common d...

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  • Hyperspectral citrus leaf disease identification method based on characteristic wavelength
  • Hyperspectral citrus leaf disease identification method based on characteristic wavelength
  • Hyperspectral citrus leaf disease identification method based on characteristic wavelength

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Embodiment Construction

[0046] The present invention will be further described below in conjunction with specific examples.

[0047] see figure 1 As shown, the hyperspectral citrus leaf disease identification method based on characteristic wavelengths provided in this example utilizes hyperspectral technology based on characteristic spectra to identify healthy leaves and citrus infected by four diseases of anthracnose, yellow spot, canker and Huanglongbing blade, comprising the steps of:

[0048] 1) Prepare healthy leaves and citrus leaves infected by the four diseases of anthracnose, yellow spot, canker and Huanglongbing, arrange the leaves of each group in the same order, and use the hyperspectral data acquisition system to collect the leaves of each group placed on the sample stage. Hyperspectral information of leaf types, such as figure 2Shown are images taken at 597.3 nm for a set of leaf samples of different types. Among them, the samples are citrus leaves of the same period, the same varie...

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Abstract

The invention discloses a hyperspectral citrus leaf disease identification method based on a characteristic wavelength. According to the method, the characteristic wavelength is selected based on waveband operation by utilizing a hyperspectral technology to realize the discrimination and detection of various diseases of citrus leaves. By establishing a disease type discrimination model, a detection result of a disease type can be obtained only by acquiring a hyperspectral image of a to-be-detected sample for preprocessing and extracting a reflectivity data model under the corresponding characteristic wavelength, and the nondestructive, rapid and accurate identification of the citrus leaf disease type can be realized. The characteristic wavelength is selected by using the correlation coefficient of a waveband operation result and a mark value, the calculation is simple, and the judgment effect of the selected characteristic wavelength is good. After hyperspectral data of the to-be-detected sample is preprocessed, the spectral value of each pixel is substituted into the model, and the disease type and distribution can be visually displayed through colors, and the method is more intuitive.

Description

technical field [0001] The invention relates to the technical field of non-destructive rapid identification of citrus leaf diseases, in particular to a hyperspectral citrus leaf disease identification method based on characteristic wavelengths. Background technique [0002] Citrus fruit is the most produced fruit in the world. During its production and development, citrus will be harmed by hundreds of diseases and insect pests, such as viral and bacterial diseases: Huanglongbing, canker, etc.; fungal diseases: anthracnose, black spot, etc. Infected citrus can quickly spread from one area to another, causing huge economic losses. In the process of plant disease control, discovery and detection is the most important step, allowing managers to control the spread of diseases in a timely and targeted manner. [0003] At present, the vast majority of disease detection is based on manual investigation. According to the external manifestations of diseased plants and pathogens, jud...

Claims

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Application Information

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IPC IPC(8): G01N21/95
CPCG01N21/95
Inventor 褚璇唐宇骆少明侯超钧庄家俊郭琪伟苗爱敏陈亚勇高升杰程至尚朱耀宗陈家政吴亮生
Owner ZHONGKAI UNIV OF AGRI & ENG
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