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

文献类型： 外文期刊

作者： Zhang, Hao 1 ; Hu, Hao 2 ; Zhang, Xiao-bin 2 ; Zhu, Lian-feng 1 ; Zheng, Ke-feng 2 ; Jin, Qian-yu 1 ; Zeng, Fu-ping 3 ;

作者机构： 1.China Natl Rice Res Inst, State Key Lab Rice Biol, Hangzhou 310006, Zhejiang, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Digital Agr Res, Key Lab Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

3.Chinese Acad Sci, Inst Subtrop Agr, Changsha 410125, Hunan, Peoples R China

关键词： Artificial neural network model;Disease index;Remote sensing;Significant wavelength;Spectral reflectance

期刊名称：ACTA PHYSIOLOGIAE PLANTARUM （ 影响因子：2.354； 五年影响因子：2.711 ）

ISSN： 0137-5881

年卷期： 2011 年 33 卷 6 期

页码：

收录情况： SCI

摘要： 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.

  • 相关文献

[1]Monitoring Leaf Chlorophyll Fluorescence with Spectral Reflectance in Rice (Oryza sativa L.). Zhang, Hao,Zhu, Lian-feng,Jin, Qian-yu,Zhang, Hao,Hu, Hao,Zheng, Ke-feng. 2011

[2]Monitoring Leaf Chlorophyll Fluorescence with Spectral Reflectance in Rice (Oryza sativa L.). Hao Zhang,Lian-feng Zhu,Hao Hu,Ke-feng Zheng,Qian-yu Jin. 2011

[3]Detecting Suaeda salsa L. chlorophyll fluorescence response to salinity stress by using hyperspectral reflectance. Zhang, Hao,Wang, Kelin,Song, Tongqing,Zhang, Hao,Wang, Kelin,Zeng, Fuping,Hu, Hao,Zhang, Xiaobin. 2012

[4]Characterizing and modeling regional-scale variations in soil salinity in the arid oasis of Tarim Basin, China. Ma, Ligang,Yang, Shengtian,Feng, Juan,Ding, Jianli,Ma, Ligang,Yang, Shengtian,Feng, Juan,Ding, Jianli,Ma, Fenglan,Li, Jiadan,Gu, Qing,Wu, Di.

[5]Estimation of above-ground biomass using HJ-1 hyperspectral images in Hangzhou Bay, China. Hao Zhang,Hao Hu,Xu-guo Yao,Ke-feng Zheng,Yi Gan. 2009

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