Novel analysis of hyperspectral reflectance data for detecting onset of pollen shed in maize

Transactions of the ASABE

Volumn 49, Issue 6, 2006, Pages 1947-1954

  Kaleita, Amy L ^a,b   Steward, Brian L ^a   Ewing, Robert P ^b   Ashlock, Daniel A ^c   Westgate, Mark E ^b   Hatfield, Jerry L ^d  

^a Iowa State University   (United States)

^b IOWA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF GUELPH   (Canada)

^d USDA ARS National Laboratory for Agriculture and the Environment   (United States)

Author keywords

Genetic programming; Partial least squares; Remote sensing; Spectral analysis; Tassel emergence

Indexed keywords

INFORMATION TRANSFER; TASSEL EMERGENCE;

CORRELATION METHODS; DATA REDUCTION; GENETIC PROGRAMMING; NEURAL NETWORKS; PLANTS (BOTANY); REMOTE SENSING; SPECTRUM ANALYSIS;

GRAIN (AGRICULTURAL PRODUCT);

ARTIFICIAL NEURAL NETWORK; DATA ASSIMILATION; GENETIC ALGORITHM; IMAGE CLASSIFICATION; MAIZE; NUMERICAL METHOD; POLLEN; REMOTE SENSING; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE;

CORN; FOREST CANOPY; NEURAL NETWORKS; PLANTS; REMOTE SENSING;

ZEA MAYS;

EID: 33947169966     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (22)

1. Burman, J. A. 1997. Non-literal pattern recognition method for hyperspectral imagery exploitation using evolutionary computing methods. In Imaging Spectrometry III: Proc. SPIE 3118: 250-261.
2. Burman, J. A. 1999. Hybrid pattern recognition method using evolutionary computing techniques applied to the exploitation of hyperspectral imagery and medical spectral data. In Image and Signal Processing for Remote Sensing V: Proc. SPIE 3781: 348-357.
3. Clevers, J. G., S. M. De Jong, G. F. Epema, F. D. Van Der Meer, W. H. Bakker, A. K. Skidmore, and K. H. Scholte. 2002. Derivation of the red edge index using the MERIS standard band setting. Intl. J. Remote Sensing 23(16): 3169-3184.
4. Demetriades-Shah, T. H., M. D. Steven, and J. A. Clark. 1990. High-resolution derivative spectra in remote sensing. Remote Sensing Environ. 33(1): 55-64.
5. Fonseca, A. E., M. E. Westgate, L. Grass, and D. L. Dornbos, Jr. 2003. Tassel morphology as an indicator of potential pollen production in maize. Online. Crop Management doi:10.1094/CM-2003-0804-01-RS.
6. Goel, P. K., S. O. Prasher, J. A. Landry, R. M. Patel, A. A. Viau, and J. R. Miller. 2003. Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Trans. ASAE 46(4): 1235-1246.
7. Goggi, A. S., P. Caragea, H. Lopez-Sanchez, M. Westgate, R. Arritt, and C. Clark. 2006. Statistical analysis of outcrossing between adjacent maize grain production fields. Field Crops Research 99(2-3): 147-157.
8. Goldberg, D. E. 1989. Sizing populations for serial and parallel genetic algorithms. In Proc. 3rd Int. Conference on Genetic Algorithms, 70-79. San Francisco, Cal.: Morgan Kaufman.
9. Ireland, D. S., D. O. Wilson, Jr., M. E. Westgate, J. S.Burris, and M. J. Lauer. 2006. Managing reproductive isolation in hybrid seed corn production. Crop Sci. 46(4): 1445-1455.
10. Leardi, R. 2000. Application of genetic algorithm-PLS for feature selection in spectral datasets. J. Chemometrics 14(5-6): 643-655.
11. Martens, H., and M. Martens. 2001. Multivariate Analysis of Quality. New York, N.Y.: Wiley.
12. Rauss, P. J., J. M. Daida, and S. Chaudhary. 2000. Classification of spectral imagery using genetic programming, In Proc. Genetic and Evolutionary Computation Conference. San Francisco, Cal.: Morgan Kaufmann.
13. Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering. 1973. Monitoring vegetation systems in the Great Plains with ERTS. In Proc. 3rd ERTS Symposium, 1: 48-62. Greenbelt, Md.: NASA SP-351.
14. Steward, B. L., R. P. Ewing, D. A. Ashlock, A. L. Kaleita, and S. M. Shaner. 2004. Range operator enabled genetic algorithms for hyperspectral analysis. In Intelligent Engineering Systems through Artificial Neural Networks: Smart Engineering Systems Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems, and Artificial Life 14: 295-300. C. H. Dagli, A. L. Buczak, D. L. Enke, M. J. Embrechts, and O. Ersoy, eds. New York, N.Y.: ASME Press.
15. Tang, L., L. Tian, and B. L. Steward. 2000. Color image segmentation with genetic algorithm for in-field weed sensing. Trans. ASAE 43(4): 1019-1027.
16. Viña, A., A. A. Gitelson, D. C. Rundquist, G. Keydan, B. Leavitt, and J. Schepers. 2004. Monitoring maize (Zea mays L.) phenology with remote sensing. Agron. J. 96(4): 1139-1147.
17. Westgate M. E., D. Ireland, B. Ashton, D. P. Todey, G. S. Takle, and C. Daniel. 2000. Predicting maize pollen travel using particulate dispersion models. In ASA-CSSA-SSSA Annual Meetings Abstracts. Madison, Wisc.: ASA-CSSA-SSSA.
18. Westgate, M. E., J. Lizaso, and W. D. Batchelor. 2003. Quantitative relationships between pollen shed density and grain yield in maize. Crop Sci. 43(3): 934-942.
19. Whiting, M. L., L. Li, and S. L. Ustin. 2004. Predicting water content using Gaussian model on soil spectra. Remote Sensing Environ. 89(4): 535-552.
20. Wold, H. 1982. Soft modelling: The basic design and some extensions. In Systems under Indirect Observations. Causality-Structure-Prediction, 1: 263-271. Amsterdam, The Netherlands: North-Holland.
21. Wolt, J. D., Y.-Y. Shyy, P. Christensen, K. S. Dormin, and M. Misra. 2005. Quantitative exposure assessment for confinement of maize biogenic systems. Environ. Biosafety Res. 3(4): 183-196.
22. Yao, H., and L. Tian. 2003. A genetic algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction. IEEE Trans. Geosci. Remote Sensing 41(6, pt. 1): 1469-1478.