| Spectral detection of electrical conductivity in jujube orchard soil based on continuum-removal and SPA |
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| DOI：10.7606/j.issn.1000-7601.2019.05.29 |
| Key Words: soil electrical conductivity spectral detection successive projections algorithm continuum removal prediction model |
| Author Name | Affiliation | | WANG Tao | College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China； South Xinjiang Agricultural Informatization Research Center, Alaer, Xinjiang 843300, China | | YU Cai-li | College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China； South Xinjiang Agricultural Informatization Research Center, Alaer, Xinjiang 843300, China | | ZHANG Nan-nan | College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China； South Xinjiang Agricultural Informatization Research Center, Alaer, Xinjiang 843300, China | | WANG Fei | College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China； South Xinjiang Agricultural Informatization Research Center, Alaer, Xinjiang 843300, China | | BAI Tie-cheng | College of Information Engineering, Tarim University, Alaer, Xinjiang 843300, China；Gembloux Agro-Bio Tech, University of Liège, Gembloux 25030，Belgium |
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| Abstract: |
| The typical extreme arid area of Alar City, Xinjiang was selected as the research object, and the soil electrical conductivity was inverted by using the soil hyperspectral characteristics. In order to accurately and quickly detect the soil electrical conductivity, the soil electrical conductivity and hyperspectral information of the red jujube planting area in Alar City, southern Xinjiang were obtained. Based on the continuum-removal, the correlation analysis method and the successive projections algorithm(SPA) were used to select the characteristics wavelength, and establish a partial least squares regression model of characteristic wavelength and soil electrical conductivity, using the root mean square error (RMSE), determination coefficient (R2) and relative analysis error (RPD) to evaluate the model effect of different processing methods. The results showed that the prediction accuracy based on the original spectrum directly using the correlation analysis method was RMSE=0.85566, R2=0.7479, RPD=2.7569. After the feature wavelength was selected by continuum-removal, the prediction accuracy of the model was RMSE=0.44490, R2=0.9500, RPD=6.4510; after using the SPA to select the characteristic wavelength based on the original spectrum, the prediction accuracy of the model was RMSE=0.31178, R2=0.9707, RPD=8.4445; the model was predicted by continuum-removal using SPA to select the characteristic wavelength. The accuracy was RMSE=0.303173, R2=0.9764, RPD=9.3215. In summary, the SPA method had strong feature wavelength selection ability. The prediction accuracy of partial least squares regression inversion model using SPA based on the continuum-removal was best, which could realize the rapid soil conductivity in Xinjiang Alar region detection. |
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