The average value of sampled wheat yield was 8.6 t/ha. Subsequently orthomosaic images were geo-referenced so that further study on stepwise regression analysis among nine wheat yield samples and five color vegetation indices (CVI) could be conducted, which showed that wheat yield correlated with four accumulative CVIs of visible-band difference vegetation index (VDVI), normalized green-blue difference index (NGBDI), green-red ratio index (GRRI), and excess green vegetation index (ExG), with the coefficient of determination and RMSE as 0.94 and 0.02, respectively.
#Multispec camera problem with ortophoto drivers
Besides, the last two orthomosaic images taken from about two weeks prior to harvesting also notified the occurrence of lodging by visual inspection, which could be used to generate navigation maps guiding drivers or autonomous harvesting vehicles to adjust operation speed according to specific lodging situations for less harvesting loss. Multi-temporal orthomosaic images indicated straightforward sense of canopy color changes and spatial variations of tiller densities.
Firstly, eight orthomosaic images covering a small winter wheat field were generated to monitor wheat growth status from heading stage to ripening stage in Hokkaido, Japan. In this study, we validated the feasibility of utilizing multi-temporal color images acquired from a low altitude UAV-camera system to monitor real-time wheat growth status and to map within-field spatial variations of wheat yield for smallholder wheat growers, which could serve as references for site-specific operations. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries.Īpplications of remote sensing using unmanned aerial vehicle (UAV) in agriculture has proved to be an effective and efficient way of obtaining field information. Low‑cost UAS platforms have great potential for use as a selection tool in plant breeding programs. Conclusion: The approaches described here for UAS imaging and extraction of proximal sensing data enable collec‑ tion of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the Mul‑ tiSpec 4C camera (r = 0.64). We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. Results: We found good correlation between the VIs obtained from UAS platforms and ground‑truth measurements and observed high broad‑sense heritability for VIs. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand‑held spectroradiometer.
The image dataset was processed using a photogrammetric pipeline based on image orientation and radio‑ metric calibration to produce orthomosaic images. We developed a semi‑automated image‑processing pipeline to extract plot level data from UAS imagery. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. For field‑ based high‑throughput phenotyping (HTP), UAS platforms can provide high‑resolution measurements for small plot research, while enabling the rapid assessment of tens‑of‑thousands of field plots. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. Background: Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture.
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