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

Elian Conejo; Jean-Pierre Frangi; Gilles de Rosny

doi:10.1364/AO.54.005453

Applied Optics
Vol. 54,
Issue 17,
pp. 5453-5460
(2015)
•https://doi.org/10.1364/AO.54.005453

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

Elian Conejo, Jean-Pierre Frangi, and Gilles de Rosny

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Elian Conejo, Jean-Pierre Frangi, and Gilles de Rosny, "Neural network implementation for a reversal procedure for water and dry matter estimation on plant leaves using selected LED wavelengths," Appl. Opt. 54, 5453-5460 (2015)

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Related Topics
Table of Contents Category
- Instrumentation, Measurement, and Metrology
Optics & Photonics Topics
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The topics in this list come from the Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Numerical approximation and analysis (000.4430)
- Instrumentation, measurement, and metrology (120.0120)
- Biological sensing and sensors (280.1415)

About this Article
History
- Original Manuscript: February 13, 2015
- Revised Manuscript: May 7, 2015
- Manuscript Accepted: May 9, 2015
- Published: June 8, 2015
Virtual Issues
Virtual Journal for Biomedical Optics Vol. 10, Iss. 6

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 $0.0027 g / {cm}^{2}$ 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.

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Constituents	Unit	Range	Mean	Std Dev
Water	% (mass)	$47.9 \to 93.5$	69.4	11.0
Water	$g / {cm}^{2}$	$0.0051 \to 0.0335$	0.0132	0.0062
Dry Matter	% (mass)	$6.5 \to 52.1$	30.6	11.0
Dry Matter	$g / {cm}^{2}$	$0.0012 \to 0.0264$	0.0056	0.0030

	Parameter Measured	Description
RAMIS	$T_{1050} R_{1050}$	T and R at 1050 nm
	$T_{1450} R_{1450}$	T and R at 1450 nm
	$T_{1550} R_{1550}$	T and R at 1550 nm
	$T_{1650} R_{1650}$	T and R at 1650 nm
ASD	$T (λ) R (λ)$	$[450 \to 2500] nm$
ASD		1 nm Resolution

Name	LOPEX	ANGERS
Year	1993	2003
Number of samples	580	276
Number of species	50	49
Optical parameters
Spectral range	400–2500 nm	400–2450 nm
Transmittance	✓	✓
Reflectance	✓	✓
Biochemistry
Chlorophyll AB measurements	✓	✓
Water measurements	✓	✓
Dry matter measurements	✓	✓
Carotene measurements	✓	✓

	$C_{w}$		$C_{m}$
Meas	$\| 2 σ \|$ (%)	RMSE	$\| 2 σ \|$ (%)	RMSE	Wavebands
RAMIS	6.98	0.0046	105.85	0.0227	All
RAMIS	3.53	0.0027	10.91	0.0025	$T_{1050}$ , $T_{1450}$
ASD	1.94	0.0023	5.70	0.0010	All
ASD	2.30	0.0021	10.66	0.0026	$T_{1050}$ , $T_{1450}$

Constituents	Unit	Range	Mean	Std Dev
Water	% (mass)	$47.9 \to 93.5$	69.4	11.0
Water	$g / {cm}^{2}$	$0.0051 \to 0.0335$	0.0132	0.0062
Dry Matter	% (mass)	$6.5 \to 52.1$	30.6	11.0
Dry Matter	$g / {cm}^{2}$	$0.0012 \to 0.0264$	0.0056	0.0030

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