Integrating geographic information system and remote sensing in predicting rice grain protein

Application of remote sensing and GIS has great potential in crop monitoring and retrospectively can set the strategies and management practices as to maximize yield and grain quality. In this study, UAV remote sensing data were utilized to predict grain protein content. Total images were differentiated into two groups as cloud free and cloud shadowed. On one hand for the cloud free samples, the vegetation index, NDVI derived from the canopy spectral reflectance was significantly correlated to the final grain protein content (R2=0.553, RMSE=0.210%, n=14). On the other hand, for cloud shadowed samples, the result demonstrated that vegetation index, NDVI was significantly correlated to the final grain protein content (R2=0.479, RMSE=0.225%, n=35). Different layers and files were created to manage, store and mapping grain protein using ArcGIS. All test fields at first and then, the NDVI image of each test field was also converted to shape file. Henceforth, the information of each field was displayed using overlap function. Therefore, protein content of rice in each field can be mapping by GIS and possibly forecasted using canopy or images spectral reflectance at grain filling stage