Abstract
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.
Highlights
Crop modeling, which is a method of obtaining quantitative knowledge of how crops grow by interacting with the environment, is a useful tool for predicting the yield and quality to help with crop management [1,2]
Unlike the results in previous studies in which green normalization difference VI (GNDVI) was most advantageous in predicting rice-protein content with linear regression [23,24], this study presented Medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI) calculated from NIR, red edge, and red as the most important variable
partial least squares regression (PLSR), RR, and artificial neural network (ANN) were applied to Unmanned aerial vehicles (UAVs)-based multispectral imagery of rice canopies in the ripening stage to develop prediction models for the rice yield and protein content
Summary
Crop modeling, which is a method of obtaining quantitative knowledge of how crops grow by interacting with the environment, is a useful tool for predicting the yield and quality to help with crop management [1,2]. Developed crop models include climatic variables such as the temperature, precipitation, and length of day [3]. Crop models are only applicable to normal patterns of the given climatic conditions, and all other conditions are assumed optimal [1]. Crop models cannot provide realistic predictions of the crop yield or quality. Spectral reflectance data containing visible, red-edge, and near-infrared (NIR) spectral bands have tried to managing agriculture over a wide area [5]
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