Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

Volumn , Issue , 2011, Pages

  Backhaus, Andreas ^a   Bollenbeck, Felix ^a   Seiffert, Udo ^a  

^a FRAUNHOFER INSTITUTE FOR FACTORY OPERATION AND AUTOMATION IFF   (Germany)

Author keywords

crop plant phenomics; Hyperspectral imaging; precision agriculture; relevance learning

Indexed keywords

CLASSIFICATION PERFORMANCE; CROP PLANTS; GENERALIZED RELEVANCE LEARNING VECTOR QUANTIZATIONS; GENETIC BACKGROUNDS; HIGH-THROUGHPUT; HYPERSPECTRAL IMAGING; LEAF AGE; NEURAL GAS; NON-INVASIVE; NUTRITIONAL CONDITIONS; PIXEL POSITION; PRECISION AGRICULTURE; RELEVANCE LEARNING; ROBUST CLASSIFICATION; SPECTRAL BAND; SPECTRAL SIGNATURE; TOBACCO LEAVE;

CROPS; NUTRITION; RADIAL BASIS FUNCTION NETWORKS; REMOTE SENSING; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; VECTOR QUANTIZATION;

NEURAL NETWORKS;

EID: 84255182436     PISSN: 21586276     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WHISPERS.2011.6080898     Document Type: Conference Paper

References (12)

1. Udo Seiffert and Felix Bollenbeck, "Clustering of hyperspectral image signatures using neural gas," Machine Learning Reports, vol. 4, no. 4, pp. 49-59, 2010.
2. Udo Seiffert, Felix Bollenbeck, Hans-Peter Mock, and Andrea Matros, "Clustering of crop phenotypes by means of hyperspectral signatures using artificial neural networks," in Proceedings of the 2nd IEEE Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing WHISPERS 2010, pp. 31-34. IEEE Press, 2010.
3. Vladimir Vapnik and Alexey Chervonenkis, Theory of Pattern Recognition, Nauka, 1974.
4. Barbara Hammer, Marc Strickert, and Thomas Villmann, "Supervised neural gas with general similarity measure," Neural Processing Letters, vol. 21, pp. 21-44, 2005.
5. Barbara Hammer and Thomas Villmann, "Generalized relevance learning vector quantization," Neural Networks, vol. 15, pp. 1059-1068, 2002.
6. John Moody and Christian J. Darken, "Fast learning in networks of locally tuned processing units," Neural Computation, vol. 1, pp. 281-294, 1989.
7. Gamal ElMasry, Ning Wang, Adel ElSayed, and Michael Ngadi, "Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry," Journal of Food Engineering, vol. 81, no. 1, pp. 98 - 107, 2007.
8. Hiroshi Okamoto, Tetsuro Murata, Takashi Kataoka, and Shun-Ichi Hata, "Plant classification for weed detection using hyperspectral imaging with wavelet analysis," Weed Biology and Management, vol. 7, no. 1, pp. 31-37, 2007.
9. David E. Rumelhart, Geoffrey. E. Hinton, and Ronald J. Williams, "Learning internal representations by error propagation.," in Parallel distributed processing: Exploration in the microstructure of cognition., D. E. Rumelhart and J. L. McClelland, Eds., pp. 318-362. MA: MIT Press., Cam-bridge, 1986.
10. Cristopher M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press., 1995.
11. Duane DiSieno, "Adding a conscience to competitive learning," in Proc. IEEE Int Neural Networks Conf, 1988, pp. 117-124.
12. Michael J. Mendenhall and Erzsébet Merényi, "Relevance-based feature extraction for hyperspectral images," IEEE Transactions on Neural Networks, vol. 19, no. 4, pp. 658-672, 2008.