Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities

Two-step-hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting

An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives

Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R2) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R2 values and lower RMSECV values for all data sets. Overall, the highest R2 values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R2 = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model.

https://www.mdpi.com/2072-4292/10/8/1249/pdf

Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield

Yield prediction of apricot using a hybrid particle swarm optimization-imperialist competitive algorithm- support vector regression (PSO-ICA-SVR) method

Zhenping Gong

Papers

Estimating nitrogen status of rice canopy using hyperspectral reflectance combined with BPSO-SVR in cold region