Multisensor Fusion of Remote Sensing Data for Crop Disease Detection

  • Dimitrios Moshou
  • Ioannis Gravalos
  • Dimitrios Kateris Cedric Bravo
  • Roberto Oberti
  • Jon S. West
  • Herman Ramon


There is an increasing pressure to reduce use of pesticides in modern crop production in order to decrease the environmental impact of current practice and to lower the cost of production. It is therefore important that spraying of chemicals only takes place when and where it is really needed. Since disease appearance in fields is frequently patchy, sprays may be applied unnecessarily to disease-free areas. The control of disease could be more efficient if disease patches within fields could first be identified and then phytosanitary chemicals are applied only to the infected areas. Recent developments in optical sensor technology and control systems provide the potential to enable direct detection of foliar diseases under field conditions and subsequent precise application of chemicals through targeted spraying.


Support Vector Machine Sensor Fusion Disease Detection Remote Sensing Data Yellow Rust 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Andrews, R., Diederich, J. and Tickle, A.B. (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8(6), 373-389.CrossRefGoogle Scholar
  2. Apan, A., Held, A., Phinn, S. and Markley, J. (2004). Detecting sugarcane orange rust disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing, 25(2), 489-498.CrossRefGoogle Scholar
  3. Blakeman, R.H., Bryson, R.J. and Dampney, P. (2000). Assessing crop condition in real time using high resolution satellite imagery. 1n: Aspects of Applied Biology 60, Remote Sensing in Agriculture (pp. 163-171). The Association of Applied Biologists, Wellesbourne, UK.Google Scholar
  4. Bravo, C., Moshou, D., West, J., McCartney, A. and Ramon, H. (2003). Detailed Spectral Reflection Information for Early Disease Detection in Wheat Fields. Biosystems Engineering, 84(2), 137-145.CrossRefGoogle Scholar
  5. Bravo, C., Moshou, D., Oberti, R., West, J., McCartney, A., Bodria, L. and Ramon, H. (2004, December). Foliar disease detection in the field using optical sensor fusion. International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal, Manuscript FP 04 008, 6. Retrieved April 7, 2011, from .
  6. Cui, D., Zhang, Q., Li, M. Hartman, G.L. and Zhao, Y. (2010). Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosystems Engineering, 107(3), 186-193.CrossRefGoogle Scholar
  7. Ding, X.Q. and Xin, S. (2006). Application research on extraction of rule from artificial neural networks for nonlinear regression. Dynamics of Continuous, Discrete and Impulsive Systems. Series A: Mathematical Analysis, 13, 565-568.Google Scholar
  8. Du, Q., French, J.V., Skaria, M., Yang, C. and Everitt, J.H. (2004). Citrus pest stress monitoring using airborne hyperspectral imagery. In: Conference Proceedings of the International Geoscience and Remote Sensing Symposia Vol. VI (pp. 39813984). Piscataway, New Jersey, IEEE.Google Scholar
  9. Fauvel, M., Benediktsson, J.A., Chanussot, J. and Sveinsson, J.R. (2008). Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 38043814.CrossRefGoogle Scholar
  10. Franke, J. and Menz, G. (2007). Multi-temporal wheat disease detection by multi- spectral remote sensing. Precision Agriculture, 8, 161-172.CrossRefGoogle Scholar
  11. Hall, D.L. (1992). Mathematical Techniques in Multisensor Data Fusion. Artech House, Boston/London, UK.Google Scholar
  12. Herrala, E., Okkonen, J., Hyvarinen, T., Aikio, M. and Lammasniemi, J. (1994). Imaging spectrometer for process industry applications. Paper presented at Optical Measurements and Sensors for the Process Industries, Frankfurt, Germany.Google Scholar
  13. Johnson, D.A., Alldredge, J.R., Hamm, P.B. and Frazier, B.E. (2003). Aerial photography used for spatial pattern analysis of late blight infection in irrigated potato circles. Phytopathology, 93(7), 805-812.CrossRefGoogle Scholar
  14. Krasnopolsky, V.M. and Schiller, H. (2003). Some neural network applications in environmental sciences. Part I: Forward and inverse problems in geophysical remote measurements. Neural Networks, 16(3-4), 321-334.Google Scholar
  15. Kung, H.Y., Hua, J.S. and Chen, C.T. (2006). Drought forecast model and framework using wireless sensor networks. Journal of Information Science and Engineering, 22(4), 751-769.Google Scholar
  16. Lee, W.S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D. and Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 74(1), 2-33.CrossRefGoogle Scholar
  17. Lofstrom, T., Johansson, U. and Niklasson, L. (2004). Rule extraction by seeing through the model. In: N.R. Pal et al. (Eds.), Lecture Notes in Computer Science 3316: Neural Information Processing (pp. 555-560). Springer, Berlin-Heidelberg, Germany.Google Scholar
  18. Loyola, R.D.G. (2006). Applications of neural network methods to the processing of Earth observation satellite data. Neural Networks, 19(2), 168-177.CrossRefGoogle Scholar
  19. Mitra, P., Shankar, B.U. and Pal, S. (2004). Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognition Letters, 25(9), 1067-1074.CrossRefGoogle Scholar
  20. Moshou, D., Bravo, C., West, J., McCartney, A. and Ramon, H. (2004). Automatic detection of yellow rust in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 44(3), 173-188.CrossRefGoogle Scholar
  21. Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A. and Ramon, H. (2005). Plant disease detection based on data fusion of hyper-spectral and multi- spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 11(2), 75-83.CrossRefGoogle Scholar
  22. Myers, V.I. (1983). Remote sensing applications in agriculture. In: R.N. Colwell (Ed.), Manual of Remote Sensing (pp. 2111-2228). American Society of Photogrammetry, Falls Church, VA.Google Scholar
  23. Nunez, H., Angulo, C. and Catala, A. (2006). Rule-based learning systems for support vector machines. Neural Processing Letters, 24(1), 1-18.CrossRefGoogle Scholar
  24. Pal, M. (2008). Ensemble of support vector machines for land cover classification. International Journal of Remote Sensing, 29(10), 3043-3049.CrossRefGoogle Scholar
  25. Ryerson, R.A., Curran, P.J. and Stephens, PR. (1997). Applications: agriculture. In: W.R. Philipson (Ed.), Manual of Photographic Interpretation (pp. 365-397). American Society for Photogrammetry and Remote Sensing, Bethesda, MD.Google Scholar
  26. Saad, E.W. and Wunsch, D.C. (2007). Neural network explanation using inversion. Neural Networks, 20(1), 78-93.CrossRefGoogle Scholar
  27. Vanderplank, J.E. (1984). Disease Resistance in Plants. Academic, New York/London, UK.Google Scholar
  28. Vapnik, V.N. (1998). Statistical Learning Theory. Wiley Interscience, New York.Google Scholar
  29. Vapnik, V.N. (1999). The Nature of Statistical Learning Theory. Springer-Verlag, New York.Google Scholar
  30. Zhang, L. and Dickinson, M. (2001). Fluorescence from rust fungi: a simple and effective method to monitor the dynamics of fungal growth in planta. Physiological and Molecular Plant Pathology, 59(3), 137-141.CrossRefGoogle Scholar
  31. Zhang, L., Huang, X., Huang, B. and Li, P. (2006). A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(10), 29502961.Google Scholar
  32. Zhang, R. and Ma, J. (2008). An improved SVM method P-SVM for classification of remotely sensed data. International Journal of Remote Sensing, 29(20), 60296036.CrossRefGoogle Scholar

Copyright information

© Capital Publishing Company 2011

Authors and Affiliations

  • Dimitrios Moshou
    • 1
  • Ioannis Gravalos
    • 2
  • Dimitrios Kateris Cedric Bravo
    • 3
  • Roberto Oberti
    • 4
  • Jon S. West
    • 5
  • Herman Ramon
    • 3
  1. 1.Department of Hydraulics, Soil Science and Agricultural EngineeringSchool of Agriculture, Aristotle UniversityThessalonikiGreece
  2. 2.Department of Biosystems EngineeringTechnological Educational Institute of Larissa, School of Agricultural TechnologyLarissaGreece
  3. 3.Division of Mechatronics, Biostatistics and Sensors Department of BiosystemsK.U. LeuvenBelgium
  4. 4.Istituto Di Ingegneria AgrariaUniversita Degli Studi di MilanoItaly
  5. 5.Plant Pathology and Microbiology DepartmentRothamsted Research HarpendenUK

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