Ghent University Academic Bibliography

Advanced

Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers

Xanthoula Eirini Pantazi, Dimitrios Moshou, Roberto Oberti, Jon West, Abdul Mouazen UGent and Dionysios Bochtis (2017) PRECISION AGRICULTURE. 18(3). p.383-393
abstract
Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. In this study, the case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features from a ground based hyperspectral imaging system. Hyperspectral images of healthy and diseased plant canopies were taken at Rothamsted Research, UK by a Specim V10 spectrograph. Five wavebands of 20 nm width were utilized for accurate identification of each of the stress and healthy plant conditions. The technique that was developed used a hybrid classification scheme consisting of hierarchical self organizing classifiers. Three different architectures were considered: counter-propagation artificial neural networks, supervised Kohonen networks (SKNs) and XY-fusion. A total of 12 120 spectra were collected. From these 3 062 (25.3%) were used for testing. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95% for all three architectures. The proposed approach aimed at sensor based detection of diseased and stressed plants so that can be treated site specifically contributing to a more effective and precise application of fertilizers and fungicides according to specific plant’s needs.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (proceedingsPaper)
publication status
published
subject
keyword
Crop disease, Machine learning, Neural networks, Nitrogen stress, Hyperspectral sensing, SUGAR-BEET DISEASES, SPECTRAL PROPERTIES, NEURAL-NETWORKS, POWDERY MILDEW, PLANT, CLASSIFICATION, IDENTIFICATION, PROTECTION, IMAGES, WHEAT
journal title
PRECISION AGRICULTURE
Precis. Agric.
volume
18
issue
3
pages
383 - 393
conference name
10th European conference on Precision Agriculture
conference location
Tel Aviv, Israel
conference start
2015-07-12
conference end
2015-07-16
Web of Science type
Article; Proceedings Paper
Web of Science id
000400083100007
ISSN
1385-2256
1573-1618
DOI
10.1007/s11119-017-9507-8
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8520759
handle
http://hdl.handle.net/1854/LU-8520759
date created
2017-05-18 09:02:23
date last changed
2017-09-06 09:44:16
@article{8520759,
  abstract     = {Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. In this study, the case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features from a ground based hyperspectral imaging system. Hyperspectral images of healthy and diseased plant canopies were taken at Rothamsted Research, UK by a Specim V10 spectrograph. Five wavebands of 20 nm width were utilized for accurate identification of each of the stress and healthy plant conditions. The technique that was developed used a hybrid classification scheme consisting of hierarchical self organizing classifiers. Three different architectures were considered: counter-propagation artificial neural networks, supervised Kohonen networks (SKNs) and XY-fusion. A total of 12 120 spectra were collected. From these 3 062 (25.3\%) were used for testing. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95\% for all three architectures. The proposed approach aimed at sensor based detection of diseased and stressed plants so that can be treated site specifically contributing to a more effective and precise application of fertilizers and fungicides according to specific plant{\textquoteright}s needs.},
  author       = {Pantazi, Xanthoula Eirini and Moshou, Dimitrios and Oberti, Roberto and West, Jon and Mouazen, Abdul and Bochtis, Dionysios},
  issn         = {1385-2256},
  journal      = {PRECISION AGRICULTURE},
  keyword      = {Crop disease,Machine learning,Neural networks,Nitrogen stress,Hyperspectral sensing,SUGAR-BEET DISEASES,SPECTRAL PROPERTIES,NEURAL-NETWORKS,POWDERY MILDEW,PLANT,CLASSIFICATION,IDENTIFICATION,PROTECTION,IMAGES,WHEAT},
  language     = {eng},
  location     = {Tel Aviv, Israel},
  number       = {3},
  pages        = {383--393},
  title        = {Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers},
  url          = {http://dx.doi.org/10.1007/s11119-017-9507-8},
  volume       = {18},
  year         = {2017},
}

Chicago
Pantazi, Xanthoula Eirini, Dimitrios Moshou, Roberto Oberti, Jon West, Abdul Mouazen, and Dionysios Bochtis. 2017. “Detection of Biotic and Abiotic Stresses in Crops by Using Hierarchical Self Organizing Classifiers.” Precision Agriculture 18 (3): 383–393.
APA
Pantazi, X. E., Moshou, D., Oberti, R., West, J., Mouazen, A., & Bochtis, D. (2017). Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers. PRECISION AGRICULTURE, 18(3), 383–393. Presented at the 10th European conference on Precision Agriculture .
Vancouver
1.
Pantazi XE, Moshou D, Oberti R, West J, Mouazen A, Bochtis D. Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers. PRECISION AGRICULTURE. 2017;18(3):383–93.
MLA
Pantazi, Xanthoula Eirini, Dimitrios Moshou, Roberto Oberti, et al. “Detection of Biotic and Abiotic Stresses in Crops by Using Hierarchical Self Organizing Classifiers.” PRECISION AGRICULTURE 18.3 (2017): 383–393. Print.