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


Because of the unreliability judgment of paddy rice’s nitrogen deficiency depending on the traditional artificial naked eye, in this article, the way of the paddy rice’s nitrogen deficiency examination based on image is put forward, to achieve the precise fast lossless detection and judgment on the paddy rice’s nitrogen. Based on the sorting function of SMV, paddy rice leaf's visible images are gathered, the texture features of image are extracted, the RBF nuclear function is chosen, the penalty coefficient C and the regularity coefficient ??are set, and the SVM sorting model is constructed. The recurrence sentencing rate to the training sample achieves 100%. The examination is caught on the test sample, and the accuracy rate of examination recognition achieve 95%, which indicates that the method of paddy rice’s nitrogen lossless examination judgment by image is effective and feasible to achieve the precise fast judgment on paddy rice’s nitrogen.


Support Vector Machine Paddy Rice Nuclear Function Penalty Coefficient Regularity Coefficient 
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. Fang Ruiming. Induction Machine Rotor Diagnosis using Support Vector Machines and Rough Set[J]. Lectures notes on Artificial Intelligence, 2006. Vol: 631~637(in Chinese)Google Scholar
  2. Ge Guangying. Algorithm of vehicle detection and pattern recognition using SVM. Computer Engineering. 2007(6):6–10(in Chinese)Google Scholar
  3. Lu Renfu, Daniel E Guyer, Randolph M Beaudry. Determination of firmness and sugar content of apples using near—infrared diffuse reflectance. Journal of Texture Studies, 2000,31:615–630(in Chinese)CrossRefGoogle Scholar
  4. Xu Guili, Mao Hanping, Li Pingping. Extracting Color Features of Leaf Color Images. Transactions of the CSAE. 2002,7:150–154(in Chinese)Google Scholar
  5. Xu Guili, MAO Hanping,LI Pingping. Application Algorithm to Extract Color Images Color and Textures Features. Computer Engineering. 2002,6:25–27(in Chinese)Google Scholar
  6. Zhang Wei Mao Hanping, LI Pingping, XIA Zhijun. Research on Extracting Color and Texture Features of Plant Nutrient Deficiency Leaves' Image. Journal of Agricultural Mechanization Research. 2003,4:60–63(in Chinese)Google Scholar
  7. Zhao Jiewen, Hu Huaiping, Zou Xiaobo.Application of support vector machine to apple classification with near—infrared spectroscopy. Transactions of the CSAE.2007, 23(4):149–152.(in Chinese)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Jiangsu University, ZhenjiangJiangsuChina

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