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Remote Sensing Science

Remote Sensing Science is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of remote sensing science and technology. The main focus of the journal is the academic papers and comments of latest improvement in the fields of basic theory, technology development and application of remote sensing science, report of latest research result, aiming at providing a good communication platform to tran... [More] Remote Sensing Science is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of remote sensing science and technology. The main focus of the journal is the academic papers and comments of latest improvement in the fields of basic theory, technology development and application of remote sensing science, report of latest research result, aiming at providing a good communication platform to transfer, share and discuss the theoretical and technical development of remote-sensing theory development for professionals, scholars, researchers and administrative staffs in this field, reflecting the academic front level, promote academic change and seize the theory, practice front line, research level and development direction of remote sensing science.

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Paper Infomation

Nitrogen Estimation Model of Apple Leaves Based on Imaging Spectroscopy

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Author: Xin Wen, Xicun Zhu, Shujing Cao, Xiaoyan Guo, Ruiyang Yu, Jingling Xiong, Dongsheng Gao

Abstract: Imaging spectrometer was used to measure the spectral data of apple leaves. The spectral reflectance of apple leaves was extracted. The nitrogen content of apple leaves was correlated with the spectral reflectance after SG smoothing first-order differential treatment. The sensitive wavelengths were selected and nitrogen content prediction models were founded. The results showed that the spectral of apple leaves with different concentration gradients were obvious. The higher nitrogen content was, the lower spectral reflectance was. Established estimation models by using the selected SG smooth first-order differential spectral sensitive wavelengths SG-FDR403, SG-FDR469, SG-FDR525, SG-FDR566, SG-FDR650, SG-FDR696, SG-FDR781, SG-FDR851, SG-FDR933 .The determined coefficient (R2) of the partial least squares model was 0.5202. The root mean square error (RMSE) of that was 2.19 and the relative error (RE) of that was 5.89%. The R2 of the support vector machine (SVM) model was 0.724. The RMSE of that was 1.94, and the RE of that was 5.13%. It is indicated that the SVM model can estimate the nitrogen content of apple leaves effectively.

Keywords: Apple Leaves, Nitrogen, Hyperspectral imaging, Support Vector Machine

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