Estimation of rice neck blasta severity using spectral reflectance based on BP-neural Network

• Autorzy: Zhang H. , Hu H. , Zhang X. , Zhu L. , Zheng K. , Jin Q. , Zeng F.
• Języki publikacji: EN

Abstrakty

EN

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 nonlinear 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.

• Wydawca: -
• Czasopismo: Acta Physiologiae Plantarum
• Rocznik: 2011
• Tom: 33
• Numer: 6
• Opis fizyczny: p.2461-2466,fig.,ref.

Twórcy

autor

Zhang H.

• State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
• Key Laboratory of Digital Agriculture, Institute of Digital Agricultural Research, Zhejiang Academy of Agricultural Sciences, Hongzhou 310021, China
• Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China

autor

Hu H.

• Key Laboratory of Digital Agriculture, Institute of Digital Agricultural Research, Zhejiang Academy of Agricultural Sciences, Hongzhou 310021, China

autor

Zhang X.

• Key Laboratory of Digital Agriculture, Institute of Digital Agricultural Research, Zhejiang Academy of Agricultural Sciences, Hongzhou 310021, China

autor

Zhu L.

• State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China

autor

Zheng K.

• Key Laboratory of Digital Agriculture, Institute of Digital Agricultural Research, Zhejiang Academy of Agricultural Sciences, Hongzhou 310021, China

autor

Jin Q.

• State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China

autor

Zeng F.

• Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China

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