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Transactions of the ASABE Vol. 49(6): 1947-1954 2006 American Society of Agricultural and Biological Engineers ISSN 0001-2351 1947
 

Summary: Transactions of the ASABE
Vol. 49(6): 1947-1954 2006 American Society of Agricultural and Biological Engineers ISSN 0001-2351 1947
NOVEL ANALYSIS OF HYPERSPECTRAL REFLECTANCE DATA
FOR DETECTING ONSET OF POLLEN SHED IN MAIZE
A. L. Kaleita, B. L. Steward, R. P. Ewing, D. A. Ashlock, M. E. Westgate, J. L. Hatfield
ABSTRACT. Knowledge of pollen shed dynamics in and around seed production fields is critical for ensuring a high yield of
genetically pure corn seed. Recently, changes in canopy reflectance using hyperspectral reflectance have been associated
with tassel emergence, which is known to precede pollen shed in a predictable manner. Practical application of this remote
sensing technology, however, requires a simple and reliable method to evaluate changes in spectral images associated with
the onset of tassel emergence and pollen shed. In this study, several numerical methods were investigated for estimating
percentage of plants with visible tassels (VT) and percentage of plants that initiated pollen shed (IPS) from remotely sensed
hyperspectral reflectance data (397 to 902 nm). Correlation analysis identified regions of the spectra that were associated
with tassel emergence and anthesis (i.e., 50% of plants shedding pollen). No single band, however, generated correlations
greater than 0.40 for either VT or IPS. Classification using an artificial neural network (ANN) was predictive, correctly
classifying 83.5% and 88.3% of the VT and IPS data, respectively. The extensive preprocessing necessary and the "black box"
nature of ANNs, however, rendered analysis of spectral regions difficult using this method. Partial least squares (PLS) analysis
yielded models with high predictive capability (R2 of 0.80 for VT and 0.79 for IPS). The PLS coefficients, however, did not
exhibit a spectrally consistent pattern. A novel range operator-enabled genetic algorithm (ROE-GA), designed to consider
the shape of the spectra, had similar predictive capabilities to the ANN and PLS, but provided the added advantage of allowing
information transfer for increased domain knowledge. The ROE-GA analysis is the preferred method to evaluate

  

Source: Ashlock, Dan - Department of Mathematics and Statistics, University of Guelph

 

Collections: Mathematics