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Neural network implementation for a reversal procedure for water and dry matter estimation on plant leaves using selected LED wavelengths

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Abstract

An inversion method based on a neural network was used to estimate water and dry matter contents on plant leaves, from transmittance and reflectance measurements, using light emitting diodes (LEDs) at specific wavelengths in NIR and FIR. The preliminary results for the predicted water content by the neural network method showed a RMSE value of 0.0027g/cm2 and |σ| value of approximately 3.53%, computed on 127 plant leaf samples over 51 species. Dry matter estimation also was performed, which showed potential implementation after future improvements. We believe this inversion method could be implemented in a portable system based on any silicon platform with the capability to perform in situ measurements on plant tissue.

© 2015 Optical Society of America

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