Agriculture faces many challenges related to the increasing food demands of a
growing global population and the sustainable use of resources in a changing
environment. To address them, we need reliable information sources, like
exploiting hyperspectral satellite, airborne, and ground-based remote sensing
data to observe phenological traits through a crops growth cycle and gather
information to precisely diagnose when, why, and where a crop is suffering
negative impacts. By combining hyperspectral capabilities with unmanned
aerial vehicles (UAVs), there is an increased capacity for providing time-critical
monitoring and new insights into patterns of crop development. However,
considerable effort is required to effectively utilize UAV-integrated
hyperspectral systems in crop-modeling and crop-breeding tasks.
Here, a UAV-based hyperspectral solution for mapping crop physiological
parameters was explored within a machine learning framework. To do this, a
range of complementary measurements were collected from a field-based
phenotyping experiment, based on a diversity panel of wild tomato (Solanum
pimpinellifolium) that were grown under fresh and saline conditions. From the
UAV data, positionally accurate reflectance retrievals were produced using a
computationally robust automated georectification and mosaicking
methodology. The resulting multitemporal UAV data were then employed to
retrieve leaf-chlorophyll (Chl) dynamics via a machine learning framework.
Several approaches were evaluated to identify the best-performing regression
supervised methods. An investigation of two learning strategies (i.e., sequential
and retraining) and the value of using spectral bands and vegetation indices
(VIs) as prediction features was also performed. Finally, the utility of UAVbased
hyperspectral phenotyping was demonstrated by detecting the effects of
salt-stress on the different tomato accessions by estimating the salt-induced
senescence index from the retrieved Chl dynamics, facilitating the identification
of salt-tolerant candidates for future investigations.
This research illustrates the potential of UAV-based hyperspectral imaging for
plant phenotyping and precision agriculture. In particular, a) developing
systematic imaging calibration and pre-processing workflows; b) exploring
machine learning-driven tools for retrieving plant phenological dynamics; c)
establishing a plant stress detection approach from hyperspectral-derived
metrics; and d) providing new insights into using computer vision, big-data
analytics, and modeling strategies to deal effectively with the complexity of the
UAV-based hyperspectral data in mapping plant physiological indicators.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/670149 |
| Date | 07 1900 |
| Creators | Angel, Yoseline |
| Contributors | McCabe, Matthew, Biological and Environmental Science and Engineering (BESE) Division, Hong, Pei-Ying, Tester, Mark A., Zarco-Tejada, Pablo |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | 2022-07-12, At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation will become available to the public after the expiration of the embargo on 2022-07-12. |
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