Efficient Assessment of Crop Spatial Variability Using UAV Imagery: A Geostatistical Approach
Precision agriculture has seen significant advancements with the integration of remote-sensing technologies. However, challenges such as real-time data availability and computing limitations persist. This study aimed to develop a standardized method for generating spatial variability maps for vineyard management using UAV (unmanned aerial vehicle) imagery. Using IDW (inverse distance weight), nadir images with geotagged locations were processed to extract spectral information. The results were analyzed using the NGRDI (normalized green-red difference index) and demonstrated that geo-interpolation methods are effective compared to traditional photogrammetry-based methods but 90% faster, highlighting their potential in real-time applications and edge computing. In addition, IDW correlation with Sentinel-2 imagery reached values as high as r = 0.8. This method offers a faster, less resource-intensive alternative to existing techniques for crop mapping, addressing the current challenges in precision agriculture.
| Main Authors: | , , |
|---|---|
| Format: | Article in monograph or in proceedings biblioteca |
| Language: | English |
| Published: |
MDPI
|
| Subjects: | Life Science, |
| Online Access: | https://research.wur.nl/en/publications/efficient-assessment-of-crop-spatial-variability-using-uav-imager |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Precision agriculture has seen significant advancements with the integration of remote-sensing technologies. However, challenges such as real-time data availability and computing limitations persist. This study aimed to develop a standardized method for generating spatial variability maps for vineyard management using UAV (unmanned aerial vehicle) imagery. Using IDW (inverse distance weight), nadir images with geotagged locations were processed to extract spectral information. The results were analyzed using the NGRDI (normalized green-red difference index) and demonstrated that geo-interpolation methods are effective compared to traditional photogrammetry-based methods but 90% faster, highlighting their potential in real-time applications and edge computing. In addition, IDW correlation with Sentinel-2 imagery reached values as high as r = 0.8. This method offers a faster, less resource-intensive alternative to existing techniques for crop mapping, addressing the current challenges in precision agriculture. |
|---|