Estimation of chlorophyll concentration in vegetation using global optimization approach

In this paper we consider the problem of estimating chlorophyll content in vegetation using an experimental optical method from noisy spectral data. It is shown that the quantitative analysis of the spectral curves for the reflection of plant leaves may be the basis for development of new methods for interpretation of the data obtained by the remote measurement of plants. A mathematical model of vegetation reflectance is proposed to estimate the chlorophyll content from spectral data. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQR) methods. An estimation is related to the local scoring procedure for the generalized additive model. A deviation measurement in risk analysis of vegetation is considered. The role of deviation and risk measures in optimization is analyzed. Experimental and simulation results are shown for different agricultural plants using a functional-parametric representation of spectral curves.

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