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Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield
摘要： Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best-performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single-time-point data.
关键词： maize yield,hyperspectral imaging,prediction accuracy,vegetation indices,Bayesian methods
更新于2025-09-23 15:23:52
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High-accuracy prediction of carbon content in semi-coke by laser-induced breakdown spectroscopy
摘要： Semi-coke, as one kind of special coal resource with relatively high concentration carbon and low volatility, plays an important role in the coal chemical industry and city clean. Laser-Induced Breakdown Spectroscopy (LIBS) has been proved as an effective way to make an online analysis for the coal products. However, the lower volatility of semi-coke makes it hard to be pressed into a slice to get a smooth surface for a uniform laser-irradiation. Therefore, it is necessary to find an effective way to realize a high-accuracy LIBS detection for semi-coke application. Herein, two feasible ways of sample preparation are tried, one easy way is directly painting semi-coke powders on a tape that suitable for online fast monitoring, and the other complicated way is to mix binder into the semi-coke powder then that the uniformly and tightly coal slices are obtained, thus to improve the repeatability of measurement. Moreover, a totally new algorithm, support vector machine (SVM) combined with partial least square (PLS) regression(SVM-PLS), is utilized to establish an effective prediction model to make a high prediction accuracy. The coefficient of determination (R2), root mean square error of prediction (RMSEP), and average relative error (ARE) are 0.944, 0.90%, and 0.80%, respectively. In comparison with the result of the traditional PLS model, the SVM residual correction greatly improves the quality of the calibration curve and makes RMSEP and ARE reduced 0.17%, thus improves the prediction accuracy, which is much better than basic PLS regression. Meanwhile, the prediction error from binder mixed semi-coke slice is significantly reduced compared to that with directly painting samples on a tape. The maximum relative errors (MRE) are 2.71% and 5.19%, and the average RSD of the characteristic peaks are 12.1% and 16.2%, respectively, indicating that the easy way with painting sample on tape has little prediction uncertainties. Finally, in a three-day random test, the average RMSEP is 1.89% and average ARE is 1.74%, which also proves the binder additive can effectively reduce the matrix effect and enhance the stability of the spectrum for semi-coke measurement. The result proposes the proper LIBS analysis on semi-coke is a feasible and promising approach for on-line prediction of such kind of coal sample.
关键词： LIBS,prediction accuracy,Laser-Induced Breakdown Spectroscopy,semi-coke,carbon content,SVM-PLS
更新于2025-09-23 15:19:57