PMID- 32829161
OWN - NLM
STAT- MEDLINE
DCOM- 20210514
LR  - 20210514
IS  - 1873-3557 (Electronic)
IS  - 1386-1425 (Linking)
VI  - 243
DP  - 2020 Dec 15
TI  - Study on terahertz spectrum analysis and recognition modeling of common 
      agricultural diseases.
PG  - 118820
LID - S1386-1425(20)30799-X [pii]
LID - 10.1016/j.saa.2020.118820 [doi]
AB  - Diseases are critical factors that affect the yield and quality of crops. 
      Therefore, it is of great research value to develop rapid and quantitative 
      methods for identification of common agricultural diseases. This exploratory 
      study involved data analysis of common fungal pathogens using identification 
      modeling based on terahertz spectrum technology. The selected pathogens were 
      Physalospora piricola, Erysiphe cichoracearum, and Botrytis cinerea, which are 
      common fungal pathogens that cause apple ring rot, cucumber powdery mildew, and 
      grape gray mold blight, respectively. Taking polyethylene as the control, the 
      terahertz time-domain spectra, and frequency-domain spectra of samples of the 
      three pathogens were both measured. The absorption and refraction characteristics 
      of these samples in the range of 0.1-2.0 THz were calculated and analyzed, and 
      samples were then divided using the KS algorithm. Terahertz spectrum-image data 
      blocks of the pathogen samples were preprocessed, and the dimensions of data were 
      reduced using non-local mean filtering and the SPA algorithm, respectively. 
      K-nearest neighbors (KNN), support vector machine (SVM), and BP neural network 
      (BPNN), and other algorithms were used for analysis of terahertz images at 
      characteristic frequencies, and for investigating the identification model. The 
      model was quantitatively evaluated, and its imaging visualization was studied. 
      The results suggest that there are significant differences among P. piricola, E. 
      cichoracearum, and B. cinerea in absorption and refraction in the terahertz band. 
      SVM modeling identification results of the three pathogens at the frequency of 
      1.376 THz were satisfactory, with an R(p) of 0.9649, RMSEP of 0.0273, and a high 
      (93.8212%) comprehensive evaluation index F1-score, and a clearly identifiable 
      visualization effect. This study demonstrated the potential of terahertz 
      spectroscopy to be used for identification of common crop pathogens and has 
      provided technical references for the rapid diagnosis and early warning of 
      agricultural diseases.
CI  - Copyright (c) 2020 Elsevier B.V. All rights reserved.
FAU - Li, Bin
AU  - Li B
AD  - Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, PR 
      China; Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of 
      Agriculture, PR China. Electronic address: Lib@nercita.org.cn.
FAU - Zhang, Dianpeng
AU  - Zhang D
AD  - Beijing Academy of Forestry and Agriculture Sciences, Beijing, PR China.
FAU - Shen, Yin
AU  - Shen Y
AD  - Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, PR 
      China; Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of 
      Agriculture, PR China.
LA  - eng
PT  - Journal Article
DEP - 20200811
PL  - England
TA  - Spectrochim Acta A Mol Biomol Spectrosc
JT  - Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
JID - 9602533
RN  - Botrytis cinerea
SB  - IM
MH  - Botrytis
MH  - Neural Networks, Computer
MH  - Spectrum Analysis
MH  - Support Vector Machine
MH  - *Terahertz Spectroscopy
OTO - NOTNLM
OT  - Crop pathogen
OT  - Identification
OT  - Imaging
OT  - Spectroscopy
OT  - Terahertz
OT  - Terahertz data-cube
COIS- Declaration of competing interest The authors declare no conflict of interest.
EDAT- 2020/08/24 06:00
MHDA- 2021/05/15 06:00
CRDT- 2020/08/24 06:00
PHST- 2020/07/20 00:00 [received]
PHST- 2020/08/02 00:00 [revised]
PHST- 2020/08/07 00:00 [accepted]
PHST- 2020/08/24 06:00 [pubmed]
PHST- 2021/05/15 06:00 [medline]
PHST- 2020/08/24 06:00 [entrez]
AID - S1386-1425(20)30799-X [pii]
AID - 10.1016/j.saa.2020.118820 [doi]
PST - ppublish
SO  - Spectrochim Acta A Mol Biomol Spectrosc. 2020 Dec 15;243:118820. doi: 
      10.1016/j.saa.2020.118820. Epub 2020 Aug 11.