Detection of Chlorophyll Content of Rice Leaves by Chlorophyll Fluorescence Spectrum Based on PCA-ANN

DOI

10.2991/icmcm-16.2016.12How to use a DOI?

Keywords

Fluorescence Spectrum; Chlorophyll;Principal Components Analysis;Artificial Neural Network

Abstract

In order to achieve the fast, nondestructive and accurate testing of rice leaves chlorophyll content, the model of detecting rice leaves' chlorophyll content was established by laser-induced chlorophyll fluorescence (LICF)spectroscopy technology and principal components analysis(PCA)combined with artificial neural network(ANN)in the present research. For comparison, multiple linear regression(MLR)was applied too. First of all, chlorophyll fluorescence spectrum and chlorophyll content of rice leaves were measured in different growth stages. Meanwhile, PCA was used to achieve the dimension reduction on spectral information, three principal components whose variance are greater than 1 and cumulative credibility is 99.81% were extracted by this method. Furthermore, due to the correlation between the chlorophyll content of leaves and the three principal components, the three principal components were used as the inputs of ANN and MLR to establish PCA-ANN model and PCA- MLR model respectively. Prediction examinations of the two models were made based on the data measured during the same period. The results show that both PCA-ANN model and PCA- MLR model can complete the forecast on the chlorophyll content,but the result of PCA- ANN model is better than the result of PCA- MLR and the minimum of its relative prediction error is 2.62%, as the maximum is 4.83%.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lina Zhou
AU  - Shuchao Cheng
AU  - Haiye Yu
PY  - 2016/12
DA  - 2016/12
TI  - Detection of Chlorophyll Content of Rice Leaves by Chlorophyll Fluorescence Spectrum Based on PCA-ANN
BT  - Proceedings of the 2016 7th International Conference on Mechatronics, Control and Materials (ICMCM 2016)
PB  - Atlantis Press
SP  - 52
EP  - 56
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmcm-16.2016.12
DO  - 10.2991/icmcm-16.2016.12
ID  - Zhou2016/12
ER  -