Evidence map›Paper›PMID 36247642›Full record

ArticleFrontiers in plant science2022

Cucumber powdery mildew detection method based on hyperspectra-terahertz.

Xiaodong Zhang, Pei Wang, Yafei Wang, Lian Hu, Xiwen Luo, Hanping Mao, Baoguo Shen

Abstract read
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Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaodong ZhangCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Pei WangCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Yafei WangCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Lian HuKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, China.
Xiwen LuoKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, China.
Hanping MaoCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Baoguo ShenCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases.

Indexed as

cucumberdisease detectionhyperspectralpowdery mildewterahertz

Identifiers

PMID36247642
PMCPMC9558005

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.