• Jun Sun
  • Hanping Mao
  • Jinjuan Liu
  • Bin Zhang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)


The method of the quantitative analysis on the paddy rice moisture condition is studied, which is based on the spectral reflectivity of the leaf crest layer. Several subsections are carried on the entire spectrum curve by the equidistance, The sensitive characteristic wave-length is selected based on the table of molecular spectrum sensitive wave band, obtains the characteristic spectral reflection index value to take as the characteristic value. The convergence rate of the BP neural network is slow, so the L-M algorithm is introduced to carry on the renewal of the neural network weights. The paddy rice water moisture quantitative analysis forecast model is established by making use of the fast study function of the L-M algorithm neural network. The forecasting results indicate that the highest prediction error of the paddy rice water content is 6.72% and the average error rate is 4.23%. The prediction effect is better than the traditional BP network arithmetic, and it can be used in the lossless inspection of paddy rice moisture.


Paddy Rice Spectral Reflectivity Leaf Water Content Algorithm Neural Network Average Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Ji Hai-yan, WANG Peng-xin, YAN Tai-lai. Estimations of Chlorophyll and Water Contents in Live Leaf of Winter Wheat with Reflectance Spectroscopy. Spectroscopy and Spectral Analysis, 2007,27(3):514–516.(in Chinese)Google Scholar
  2. Michio S,T suyoshi A. Seasonal visible near-infrared and mid-infrared spectral of rice canopies in relation to LAI and above ground dry phytomass. Remote Sensing of Environment, 1989,27:119–127CrossRefGoogle Scholar
  3. Tian Yongchao, CAO Weixing, JIANG Dong, ZHU Yan. Relationship between Canopy Reflectance and Plant Water Content in Rice under different Soil Water and Nitrogen Conditions. 2005, 29(2):318–323(in Chinese)Google Scholar
  4. Yuan Jin-li,GUO Zhi-tao.Stored-grain pests classification based on L-M neural networks. Agricultral Network Information. 2007(6):29–32(in Chinese)Google Scholar
  5. Zhang Bing, Yuan Shouqi, Cheng Li. Model for predicting crop water requirements by using L- M optimization algorithm BP neural network. Transactions of the Chinese Society of Agricultural Engineering. 2004 (6):73–76(in Chinese)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Key Laboratory of Modern Agricultural Equipment and Technology, inistry of Education & Jiangsu Province, Jiangsu UniversityZhenjiangChina
  2. 2.School of Electrical and Information Engineering ,Jiangsu universityZhenjiangChina
  3. 3.ChangZhou Institute of TechnologyChangzhouChina
  4. 4.School of Electrical and Information Engineering, JiangsuUniversity, ,,Jiangsu,Province,China,Tel:, Email :ZhenjiangChina

Personalised recommendations