samples [32]. For spectral measurement, the powder of each leaf was placed on the probe and covered with a black cover. All spectral data ranged from 400 to 2400 nm with 5 nm resolution. The data set is freely available at the official website of ecological spectral information system (EcoSIS) and can be obtained with the link: https://ecosis.org/package /fresh-and-dry-pepper-leaf-spectra-with-associated-potassium-and-nit rogen-measurements. 2.2. Data analysis The data were partitioned to calibration (60%) and test (40%) set using the duplex algorithm [33]. The reflectance data were used directly for the data processing as using chemometric pre-processing methods may remove the
Open resource ↗EcoSIS · pdf-raw-page:2 lines:1-78Unverified paper record
Improved prediction of potassium and nitrogen in dried bell pepper leaves with visible and near-infrared spectroscopy utilising wavelength selection techniques.
Talanta · 4 Dec 2020 · 10.1016/j.talanta.2020.121971
Abstract
Wet chemistry analysis of agricultural plant materials such as leaves is widely performed to quantify key chemical components to understand plant physiological status. Visible and near-infrared (Vis-NIR) spectroscopy is an interesting tool to replace the wet chemistry analysis, often labour intensive and time-consuming. Hence, this study accesses the potential of Vis-NIR spectroscopy to predict nitrogen (N) and potassium (K) concentration in bell pepper leaves. In the chemometrics perspective, the study aims to identify key Vis-NIR wavelengths that are most correlated to the N and K, and hence, improves the predictive performance for N and K in bell pepper leaves. For wavelengths selection, six different wavelength selection techniques were used. The performances of several wavelength selection techniques were compared to identify the best technique. As a baseline comparison, the partial least-square (PLS) regression analysis was used. The results showed that the Vis-NIR spectroscopy has the potential to predict N and K in pepper leaves with root mean squared error of prediction (RMSEP) of 0.28 and 0.44%, respectively. The wavelength selection in general improved the predictive performance of models for both K and N compared to the PLS regression. With wavelength selection, the RMSEP's were decreased by 19% and 15% for N and K, respectively, compared to the PLS regression. The results from the study can support the development of protocols for non-destructive prediction of key plant chemical components such as K and N without wet chemistry analysis.
Plant phenotyping relevance
Vis-NIR分光と波長選択・回帰モデルにより、ベル pepper葉のN・K濃度という植物形質を非破壊推定する方法の開発・比較検証が中心である。
abstractthis study accesses the potential of Vis-NIR spectroscopy to predict nitrogen (N) and potassium (K) concentration in bell pepper leaves.
abstractThe performances of several wavelength selection techniques were compared to identify the best technique.
abstractThe results from the study can support the development of protocols for non-destructive prediction of key plant chemical components such as K and N without wet chemistry analysis.
Code and data availability
The paper's Vis-NIR spectra of 119 dried bell pepper leaves with reference K and N measurements are explicitly stated to be freely available on EcoSIS, making it a public, paper-specific phenotyping dataset. The MATLAB Central link refers only to third-party generic wavelength-selection code, not authors' analysis code
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