NDLI: The prediction of rice leaf's nitrogen content based on leaf spectrum on the heading stage

Content Provider	IEEE Xplore Digital Library
Author	Sun Jun Lu Bing Wu Xiaohong
Copyright Year	2010
Description	Author affiliation: College of Electric and Information Engineering, Jiangsu University, Zhenjiang 212013, China (Lu Bing; Wu Xiaohong) \|\| Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University, Zhenjiang, 212013, China (Sun Jun)
Abstract	The change of crops' nitrogen content can cause the surface of crop leaf and the physiological characteristics of the internal organization to change, thus can cause the spectrum reflection characteristic of the crop leaf to change. In this paper, the amount of fertilizer was controlled, and nitrogen-containing samples of the rice cultivation experiment was conducted to study the relevant relations of the reflection spectrum of the rice leaf and the nitrogen content of the rice leaf in the earing period. BP network and LM neural network, Bayesian neural network are used to set up prediction models of rice leaf's nitrogen content, and a comparative analysis of the network training situation and the predicted results are carried on. The results show that LM neural network converges faster than BP neural network, and convergence precision of Bayesian neural network is higher than BP neural network's. In terms of prediction accuracy, LM neural network is the best.
Starting Page	2704
Ending Page	2707
File Size	143059
Page Count	4
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Reflectivity Training Leaf Spectrum Bayesian methods Artificial neural networks Reflection Agriculture Nitrogen Content Paddy Rice Nitrogen Heading Stage
Content Type	Text
Resource Type	Article

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