This study was conducted to reset the transplanting period to produce high-quality rice in the South regions due to the rise in air temperature caused by global warming. From May 15 to June 25, transplants were performed five times every ten days. Quantity and quality were comprehensively reviewed, including watering season, ripened grain rate, head rice rate, head rice yield, protein content, and palatability. In the southern plains, medium-late and medium maturing cultivars yielded the most when transplanted on June 15, while early maturing cultivars yielded the most when transplanted on June 25. Considering the quality and quantity of rice, the optimal transplanting window for medium-late maturing cultivars is June 10-15, for medium maturing cultivars it is June 15-20, and for early maturing cultivars it is June 25. In the middle of the southern region, the highest yield was on June 25 for medium-late maturing and June 15 for medium maturing and early maturing. However, considering the quality, the best time for transplanting is June 10 for medium-late maturing, June 5 for medium maturing, and June 15 for early maturing. As a result of the rise in average temperature, the transplantation period was delayed by at least 4 to 16 days in all varieties and regions relative to the traditional transplantation duration. In summary, it is determined that a suitable transplanting period must be established in response to the increase in average temperature to ensure the production of high-quality rice.

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.