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.
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Ahmed, F., Al-Mamun, H. A., Bari, H. A. S. M., Hossain, E., & Kwan, P. (2012). Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection, 40, 98–104.
Baker, J., Deng, L., Glass, J., Khudanpur, S., Lee, C. H., Morgan, N., et al. (2009). Developments and directions in speech recognition and understanding, Part 1 [DSP Education].F. IEEE Signal Processing Magazine, 26(3), 75–80.
Blakeman, R. H., Bryson, R. J., & Dampney, P. (2000). Assessing crop condition in real time using high resolution satellite imagery. Aspects of Applied Biology, Remote Sensing in Agriculture, 60, 163–171.
Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 88(4), 677–684.
Chaerle, L., Hagenbeck, D., De Bruyne, E., Valcke, R., & Van Der Straeten, D. (2004). Thermal and chlorophyll-fluorescence imaging distinguish plant–pathogen interactions at an early stage. Plant Cell Physiology, 45(7), 887–896.
Cibula, W. G., & Carter, G. A. (1992). Identification of a far-red reflectance response to ectomycorrhizae in slash pine. International Journal of Remote Sensing, 13, 925–932.
Dumont, K., & De Baerdemaeker, J. (2001). In field wheat nitrogen assessment using hyperspectral imaging techniques. In G. Grenier & S. Blackmore (Eds.), Proceedings of the third European conference on precision agriculture (pp. 905–910). Montpellier, France: Agro Montpellier ENSAM.
Franke, J., Gebhardt, S., Menz, G., & Helfrich, H.-P. (2009). Geostatistical analysis of the spatiotemporal dynamics of powdery mildew and leaf rust in wheat. Phytopathology, 99(8), 974–984.
Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327, 828–831.
Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76–87.
Hillnhütter, C., & Mahlein, A. K. (2008). Early detection and localization of sugar beet diseases: New approaches. Gesunde Pflanzen, 60(4), 143–149.
Huang, Y., Lan, Y., Thomson, S. J., Fang, A., Hoffmann, W. C., & Lacey, R. E. (2010). Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture, 71(2), 107–127.
Jensen, J. R. (2007). In D. Kaveney (Ed.), Remote sensing of the environment: An earth resource perspective (2nd ed.). Upper Saddle River, NJ, USA: Prentice Hall.
Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 74, 2–33.
Lorenzen, B., & Jensen, A. (1989). Changes in spectral properties induced in Barley by cereal Powdery Mildew. Remote Sensing of Environment, 27, 201–209.
Mahlein, A.-K., Oerke, E.-C., Steiner, U., & Dehne, H.-W. (2012a). Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 133(1), 197–209.
Mahlein, A.-K., Steiner, U., Hillnhütter, C., Dehne, H.-W., & Oerke, E.-C. (2012b). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods, 8(1), 3.
Masoni, A., Laura, E., & Mariotti, M. (1996). Spectral properties of leaves deficient in iron, sulphur, magnesium and manganese. Agronomy Journal, 88(6), 937–943.
Melssen, W., Wehrens, R., & Buydens, L. (2006). Supervised Kohonen networks for classification problems. Chemometrics and Intelligent Laboratory Systems, 83, 99–113.
Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., et al. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real Time Imaging Journal: Special Issue on Spectral Imaging II, 11(2), 75–83.
Moshou, D., Bravo, C., Wahlen, S., West, J., McCartney, A., De Baerdemaeker, J., et al. (2006). Plant stress type identification based on optical sensing and self-organising neural networks. Precision Agriculture, 7(3), 149–164.
Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., & Ramon, H. (2004). Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 44, 173–188.
Oerke, E.-C., & Dehne, H.-W. (2004). Safeguarding production—Losses in major crops and the role of crop protection. Crop Protection, 23, 275–285.
Polischuk, V. P., Shadchina, T. M., Kompanetz, T. I., Budzanivskaya, I. G., & Sozinov, A. A. (1997). Changes in reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral infection. Archives of Phytopathology and Plant Protection, 31(1), 115–119.
Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99.
Sasaki, Y., Okamoto, T., Imou, K., & Torii, T. (1998). Automatic diagnosis of plant disease—Spectral reflectance of healthy and diseased leaves. In Proceedings European Society of Agricultural Engineers conference, Oslo, Norway.
Waske, B., van der Linden, S., Benediktsson, J. A., Rabe, A., & Hostert, P. (2010). Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 48(7), 2880–2889.
Wu, D., Feng, L., Zhang, C., & He, Y. (2008). Early detection of Botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy. Transactions of the ASABE, 51(3), 1133–1139.
Zhang, M., & Meng, Q. (2011). Automatic citrus canker detection from leaf images captured in field. Pattern Recognition Letters, 32, 2036–2046.
Zupan, J., Novic, M., & Gasteiger, J. (1995). Neural networks with counter-propagation learning strategy used for modelling. Chemometrics and Intelligent Laboratory Systems, 27, 175–187.
Acknowledgements
The research presented was carried out in the framework of Project FARMFUSE of ICT AGRI 2 ERANET, funded through GSRT (Greece) and DEFRA (UK).
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Pantazi, X.E., Moshou, D., Oberti, R. et al. Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers. Precision Agric 18, 383–393 (2017). https://doi.org/10.1007/s11119-017-9507-8
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DOI: https://doi.org/10.1007/s11119-017-9507-8