Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery
| Autor: | Jinwoo Nam, Ye Seong Kang, Deok-Gyeong Seong, Seong-Tae Lee, Chan Seok Ryu, Si Hyeong Jang, Young-Gwang Kim |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
regression model
010504 meteorology & atmospheric sciences Mean squared error rice-protein content Science Multispectral image Red edge Regression analysis mutual prediction 04 agricultural and veterinary sciences Vegetation multispectral imagery 01 natural sciences Regression Normalized Difference Vegetation Index rice yield Partial least squares regression Statistics 040103 agronomy & agriculture 0401 agriculture forestry and fisheries General Earth and Planetary Sciences 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Remote Sensing, Vol 13, Iss 1508, p 1508 (2021) Remote Sensing Volume 13 Issue 8 Pages: 1508 |
| ISSN: | 2072-4292 |
| Popis: | Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking. |
| Databáze: | OpenAIRE |
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