Using UAV-Based Imagery to Determine Volume, Groundcover, and Growth Rate Characteristics of Lentil (Lens culinaris Medik.)
| dc.contributor.advisor | Shirtliffe, Steve | |
| dc.contributor.advisor | Bett, Kirstin | |
| dc.contributor.committeeMember | Chibbar, Ravindra | |
| dc.contributor.committeeMember | Stanley, Kevin | |
| dc.contributor.committeeMember | Congreves, Kate | |
| dc.contributor.committeeMember | Soolanayakanahally, Raju | |
| dc.creator | Nielsen, Karsten M.E. | |
| dc.creator.orcid | 0000-0002-8769-3369 | |
| dc.date.accessioned | 2020-05-27T21:50:27Z | |
| dc.date.available | 2021-05-27T06:05:10Z | |
| dc.date.created | 2020-05 | |
| dc.date.issued | 2020-05-27 | |
| dc.date.submitted | May 2020 | |
| dc.date.updated | 2020-05-27T21:50:27Z | |
| dc.description.abstract | Plant growth rate is an essential phenotypic parameter for crop physiologists and plant breeders to understand in order to quantify potential crop productivity based on specific stages throughout the growing season. While plant growth rate information can be attained though manual collection of biomass, this procedure is rarely performed due to the prohibitively large effort and destruction of plant material that is required. Unmanned Aerial Vehicles (UAVs) offer great potential for rapid collection of imagery which can be utilized for quantification of plant growth rate. In this study, six diverse lines of lentil were grown in three replicates of microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery of each biomass collection time point was collected from a UAV and utilized to produce stitched two-dimensional orthomosaics and three-dimensional point clouds. Analysis of this imagery produced quantification of groundcover and vegetation volume on an individual plot basis. Comparison with manually-measured above-ground biomass suggests strong correlation, indicating great potential for UAVs to be utilized in plant breeding programs for evaluation of groundcover and vegetation volume. Nonlinear logistic models were fit to multiple data collection points throughout the growing season. The growth rate and G50, which is the number of growing degree days (GDD) required to accumulate 50 % of maximum growth, parameters of the model are capable of quantifying growth rate, and have potential utility in plant research and plant breeding programs. Predicted maximum volume was identified as a potential proxy for whole-plot biomass measurement. Six new phenotypes have been described that can be accurately and efficiently collected from field trials with the use of UAV’s or other overhead image-collection systems. These phenotypes are; Area Growth Rate, Area G50, Area Maximum Predicted Growth, Volume Growth Rate, Volume G50, and Volume Maximum Predicted Growth. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10388/12867 | |
| dc.subject | Heigh-throughput phenotyping | |
| dc.subject | HTP | |
| dc.subject | Lentil | |
| dc.subject | Drone | |
| dc.subject | UAV | |
| dc.subject | Structure from Motion | |
| dc.subject | NDVI | |
| dc.subject | Volume | |
| dc.subject | Biomass | |
| dc.title | Using UAV-Based Imagery to Determine Volume, Groundcover, and Growth Rate Characteristics of Lentil (Lens culinaris Medik.) | |
| dc.type | Thesis | |
| dc.type.material | text | |
| local.embargo.terms | 2021-05-27 | |
| thesis.degree.department | Plant Sciences | |
| thesis.degree.discipline | Plant Sciences | |
| thesis.degree.grantor | University of Saskatchewan | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.Sc.) |