Study of Quantitative Analysis for Moisture Content in Winter Wheat Leaves Using MSC-ANN Algorithm

  • Hao Ma
  • Haiyan Ji
  • Xue Liang
  • Zhenhong Rao
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 369)


Reflectance spectra of winter wheat leaves specimens was acquired with portable spectroradiometer and integral sphere, after pretreatment with the method of multiplicative scatter correction(MSC), the principal components calculated were used as the inputs of artificial neural networks to build the Back–Propagation artificial neural networks model(BP-ANN), which can be used to predict moisture content of winter wheat leaves very well. In the article we made a study of quantitative analysis for moisture content of winter wheat leaves in booting and milk stage. The correlation coefficient(r) of predicted set in booting stage was 0.918, the standard deviation(SD) was 0.995 and the relative standard deviation(RSD) was 1.35%. And in milk stage r= 0.922, SD = 2.24, RSD = 3.37%. The model can truly predict the content of water in winter wheat leaves. Compared with the classical method, the artificial neural networks can build much better predicted model.


MSC ANN Moisture Content Reflectance spectrum Quantitative analysis 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Hao Ma
    • 1
  • Haiyan Ji
    • 1
  • Xue Liang
    • 1
  • Zhenhong Rao
    • 2
  1. 1.Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of EducationChina Agricultural UniversityBeijingChina
  2. 2.College of ScienceChina Agricultural UniversityBeijingChina

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