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 
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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

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