Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network |
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Authors: | Hao Zhang Hao Hu Xiao-bin Zhang Lian-feng Zhu Ke-feng Zheng Qian-yu Jin Fu-ping Zeng |
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Institution: | (1) State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, 310006, China;(2) Key Laboratory of Digital Agriculture, Institute of Digital Agricultural Research, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China;(3) Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China; |
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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. |
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