Crop improvement is necessary for food security as
the global population is expected to exceed 9 billion by 2050. Limitations in water resources and more frequent
droughts and floods will make it increasingly difficult to manage agricultural
resources and increase yields. Therefore, we must improve our ability to monitor
agronomic research plots and use the information they provide to predict
impacts of moisture stress on crop growth and yield. Towards this end, agronomists
have used reductions in leaf expansion rates as a visible ‘plant-based’
indicator of moisture stress. Also, modeling researchers have developed crop models
such as AquaCrop to enable quantification of the severity of moisture stress
and its impacts on crop growth and yield. Finally, breeders are using Unmanned
Aircraft Systems (UAS) in field-based High-Throughput Phenotyping (HTP) to
quickly screen large numbers of small agronomic research plots for traits
indicative of drought and flood tolerance. Here we investigate whether soybean
canopy cover and color time series from high-resolution UAS ortho-images can be
collected with enough spatial and temporal resolution to accurately quantify
and differentiate agronomic research plots, pinpoint the timing of the onset of
moisture stress, and constrain crop models such as AquaCrop to more accurately
simulate the timing and severity of moisture stress as well as its impacts on
crop growth and yield. We find that canopy cover time series derived from
multilayer UAS image ortho-mosaics can reliably differentiate agronomic
research plots and pinpoint the timing of reductions in soybean canopy
expansion rates to within a couple of days. This information can be used to
constrain the timing of the onset of moisture stress in AquaCrop resulting in a
more realistic simulation of moisture stress and a lower likelihood of
underestimating moisture stress and overestimating yield. These capabilities
will help agronomists, crop modelers, and breeders more quickly develop
varieties tolerant to moisture stress and achieve food security.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8023478 |
| Date | 10 June 2019 |
| Creators | Anthony A Hearst (6620090) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Remote_Sensing_of_Soybean_Canopy_Cover_Color_and_Visible_Indicators_of_Moisture_Stress_Using_Imagery_from_Unmanned_Aircraft_Systems/8023478 |
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