To advance crop monitoring techniques in horticultural tree crops, earlier
research has examined the relationship between crop vigor (height, canopy den-
sity, health) as assessed by remote sensing technologies and aspects such as fruit
quality and yield requirements. In recent years, structure-from-motion image pro-
cessing techniques have been widely used to generate orthomosaics and 3D point
clouds from RGB and multispectral (MS) imagery acquired by unmanned aerial
vehicles. However, this process requires a lot of computing power and can be
expensive, especially for large commercial orchards. However, studies have been
scarce comparing the accuracy of different remote sensing technologies in deter-
mining tree height and plant area index. Light detection and ranging (LiDAR)
is an alternative method to generate 3D point clouds that requires less compu-
tational power. This study assessed the accuracy, processing parameters, and
limitations of UAV-based RGB, MS, and LiDAR data for measuring cherry trees’
height and plant area index in a high-density orchard in Malauc`ene, southeastern
France. Furthermore, the plant area index changes of 5 different cherry culti-
vars were assessed during the growth cycle. Overall, the LIDAR data provided
the highest accuracy for tree height measurements around harvest (R² = 0.923,
RMSE = 0.215 m) and the beginning of leaf senescence (R² = 0.863, RMSE =
0.218 m). LiDAR-derived plant area index also produced the best accuracy at
May (R² = 0.48 and RMSE = 0.42) and October (R² = 0.45 and RMSE = 0.59).
Our findings demonstrate that UAV-based LiDAR data provide an effective and
rapid means for measuring cherry tree height and plant area index over time.
Such information can serve as a general indicator of tree health and aid growers
in making informed agricultural crop monitoring and management decisions.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/692331 |
| Date | 05 1900 |
| Creators | Veiga De Camargo, Fabio |
| Contributors | McCabe, Matthew, Biological and Environmental Science and Engineering (BESE) Division, Johansen, Kasper, Al-Ghamdi, Sami |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Relation | N/A |
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