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

(2017) PRECISION AGRICULTURE. 18(3). p.383-393
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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.
Keywords
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

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Citation

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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.
@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},
}

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