NDLI: Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network

Content Provider	Springer Nature Link
Author	Zhang, Hao Hu, Hao Zhang, Xiao bin Zhu, Lian feng Zheng, Ke feng Jin, Qian yu Zeng, Fu ping
Copyright Year	2011
Abstract	Estimation of rice disease using spectral reflectance is important to non-destructive, rapid, and accurate monitoring of rice health. In this study, the rice reflectance data and disease index (DI) were determined experimentally and analyzed by single wave correlation, regression model and neural network model. The result showed that raw spectral reflectance and first derivative reflectance (FDR) difference of the rice necks under various disease severities is clear and obvious in the different spectral regions. There was also significantly negative or positive correlation between DI and raw spectral reflectance, FDR. The regression model was built with raw and first derivative spectral reflectance, which was correlated highly with the DI. However, due to rather complicated non-linear relations between spectral reflectance data and DI, the results of DI retrieved from the regression model was not so ideal. For this reason, an artificial neural network model (BP model) was constructed and applied in the retrieval of DI. For its superior ability for solving the non-linear problem, the BP model provided better accuracy in retrieval of DI compared with the results from the statistic model. Therefore, it was implied that the rice neck blasts could be predicted by remote sensing technology.
Starting Page	2461
Ending Page	2466
Page Count	6
File Format	PDF
ISSN	01375881
Journal	Acta Physiologiae Plantarum
Volume Number	33
Issue Number	6
e-ISSN	18611664
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-05-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network model Disease index Remote sensing Significant wavelength Spectral reflectance Agriculture Plant Pathology Plant Physiology Plant Anatomy/Development Plant Biochemistry Plant Genetics & Genomics
Content Type	Text
Resource Type	Article
Subject	Physiology Plant Science Agronomy and Crop Science

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