Fast Measurement of Fruit Tree Nitrogen Status Based on Multi-Color LEDs and BP-ANN

Article Preview

Abstract:

A new optical instrument for fast measurement of fruit tree nitrogen status was designed and fabricated. Multi-color LEDs were used as light source, a portable spectrophotometer as optical detector, and an optic fiber as light signal transmission medium. In the paper, the principle of using multi-color LEDs for the measurement of fruit tree nitrogen status was first introduced. Then, the method of using error back propagation artificial neural network (BP-ANN) for calibration modeling was elaborated. Reflective light intensities of Multi-color LEDs were taken as the incoming signals to BP-ANN. The structure of BP-ANN with three layers has been optimized to minimize its calibration error. In the test, total 200 leave samples picked from Huang-hua pear trees planted in three orchards with different levels of nitrogen fertilizing. Among them, 150 samples were selected randomly out for calibration set with the remaining 50 for prediction set. The result shows that correlation coefficient of R2 between predicted and measured values of nitrogen content reaches 0.93 with maximum prediction error less than 3.36(SPAD). The study suggests that the new optical method integrating multi-color LEDs with BP-ANN is promising for fast diagnosis of fruit tree mineral nutrition status.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 179-180)

Pages:

1364-1367

Citation:

Online since:

January 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Information on http: /www. onfruit. com/pic/photo/3163. html.

Google Scholar

[2] H. Yang, J. Yao, Y. He: Spectr. Sp. A Vol. 29 (2009), p.1607.

Google Scholar

[3] H. Yang, Y. He, Y. Chen, P. Lin and D. Wu: Spectr. Sp. A Vol. 28 (2008), p.1232.

Google Scholar

[4] H. Yang, D. Wu and Y. He, in: MIPPR2007: Pattern Recognition and Computer Vision, edited by S.J. Maybank, volume 6788 of Proceedings of the Society of Photo-optical Instrumentation Engineering (SPIE), SPIE International Society of Optical Engineering Publishers (2007).

Google Scholar

[5] H. Yang and Y. He, in: Proceedings of the 9th International Conference for Young Computer Scientists, edited by G.J. Wang, IEEE Computer Society Publisher, CA (2008).

Google Scholar

[6] H. Yang and Y. He, in: Computer and Computing Technologies in Agriculture II, edited by D. Li, Volume 293 of International Federation for Information Processing, Springer (2009).

Google Scholar

[7] H. Yang and Y. He, in: Proceedings of 2008 International Conference on Informationization, Automation and Electrification in Agriculture, edited by H. Zhang, Orient Acad Forum, Austrilia (2008).

Google Scholar

[8] H. Yang and Y. He, in: 2008 IEEE International Conference on Automation and Logistics, edited by IEEE, NY (2008).

Google Scholar

[9] H. Yang, J. Wu, X. Chen and Y. He, in: 2008 7th World Congress on Intelligent Control and Automation, edited by IEEE, NY (2008).

Google Scholar

[10] H. Yang and Y. He, in: CISP 2008: 1st International Congress on Image and Signal Processing, edited by D. Li, IEEE Computer Society, CA (2008).

Google Scholar

[11] Information on http: /www. camo. com.

Google Scholar

[12] Information on http: /www. specmeters. com/Chorophyll_Mete/Minolta_SPAD_502_Meter. html.

Google Scholar

[13] Information on http: /www. mathworks. co. uk/products/neuralnet.

Google Scholar