## 4 Results  
### Stoneville, Mississippi  
Figure 2 shows orthomosaic images (RGB, NIR, CIR (Color-Infrared), and NDVI) over the USDA-ARS research farm area at Stoneville, Mississippi in September 2016. The original images for the mosaicking were acquired from a dual GoPro digital imaging system operated on the Air Tractor 402B agricultural airplane. The images shown in Figure 2, especially NDVI, clearly indicate harvested corn fields, matured soybean fields, and vegetated cotton fields in a typical September crop growth in this area. The synchronized FLIR Vue Pro 640 13 mm thermal camera scanned over the farm area at the same time. This camara is a non-radiometric thermal imager, which displays temperature field distributions (intensity of thermal radiation in captured scenes) without the possibility of any other correction. This thermal camera could be further replaced a radiometric unit, such as FLIR Vue Pro 640 R, which can provide thermal images that contain accurate, calibrated temperature data embedded in every pixel. This type of portable radiometric thermal camera has been used more often on small UAVs in recent years. Figure 3 shows the orthomosaic thermal image over the USDA-ARS research farm area at Stoneville, Mississippi in May 2017. This image clearly indicated the thermal radiation (relative temperature difference) over vegetated corn fields and soybean and cotton seedling fields in a typical May crop growth in this area. This resolution of the thermal images is much coarser than the GoPro's, but it is comparable with the imagery of high-resolution satellites in multispectral bands. Since 2012, UAVs have been used to fly over about 12 fields at various sizes, from 7K to 130K m\({}^{2}\), in the farm area for low-altitude remote sensing of crop growth monitoring. The fields flow by UAVs were used to conduct various studies of precision agriculture, such as crop field surveys, crop field scouting, crop species identification, crop health mapping, and crop yield estimation. Crop yield estimation or prediction is a conventional task of agronomy for crop production management. Traditionally, yield estimation is based on empirical or mechanistic crop modeling, using inputs of crop growth status, crop management, and weather/climate. In recent years, more remote sensing data and information, especially from UAVs, are generated for creating innovative approaches for improved crop yield estimation. Crop plant height is also an important indicator of crop yield potential. To estimate crop plant height, images collected on a UAV are processed for 3D point cloud data. With interpolation of 3D point clouds, a digital terrain model (DTM) and a digital surface model (DSM) can be calculated based on UAV photogrammetry integrated with a structure from motion (SfM) technique. The difference in DSM and DTM provides the data for crop plant height estimation from UAV image data. This method has been used to estimate soybean and  
Figure 2: Orthomosaic images (2 September 2016) of the research farm area at Stoneville, Mississippi. cotton plant heights in the fields of the research farm area, and the estimated plant heights were used to estimate crop yields [40; 41]. Furthermore, UAV-based remote sensing could help improve crop yield estimation based on agronomic principles. In agronomy, crop yield can be estimated using measured yield components [42]. For example, yield of crops such as sorghum can be estimated based on effective panicle number per unit area, average number of grains per panicle, seed setting rate, and single grain weight. Among these components, effective panicle number per unit area and spikelet number per panicle play the most important role in yield formation. With high-resolution UAV imagery of crop fields, highly accurate characterization of plant yield components is possible.