Agricultural remote sensing is an important issue, as techniques related to smart/precision agriculture could improve the quality of rice. This study aimed to explore models that consider nitrogen content in the canopy, and the transport/accumulation of assimilation products in grain, to estimate the protein content of brown rice based on UAV remote sensing and meteorological observation data.
The conclusions of this study were as follows: (1) Examination of the optimum observation time for protein estimation found that the normalized difference vegetation index (NDVI) at the heading stage was most correlated with protein content (PC). NDVI at day 30 after heading stage was second highest. Both observation times saw a small impact of fluctuation of the growing stage, due to the difference in rice planting time. (2) As a result of integration of NDVI at the heading stage and temperature data at the grain-filling stage, in Koshihikari, average temperature after 5-20 days from heading stage was most correlated with PC. In Fusaotome and Fosakogane, average temperature after 0-20 days from heading stage was most correlated with PC. (3) In this study, higher temperature at the grain-filling stage decreased PC. On the other hand, the influence of temperature during grain-filling stage on PC was much smaller than that of NDVI (nitrogen condition) on PC.
