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NAPPN Annual Conference Abstract: Dissecting lentil crop growth across multi- environment trials using unoccupied aerial vehicles
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  • Sandesh Neupane,
  • Derek M Wright,
  • Tania Gioia,
  • Steve Shirtliffe,
  • Kirstin Bett
Sandesh Neupane
Department of Plant Sciences, University of Saskatchewan

Corresponding Author:sandesh.neupane@usask.ca

Author Profile
Derek M Wright
Department of Plant Sciences, University of Saskatchewan
Tania Gioia
School of Agriculture, Forestry, Food and Environmental Sciences, University of Basilicata
Steve Shirtliffe
Department of Plant Sciences, University of Saskatchewan
Kirstin Bett
Department of Plant Sciences, University of Saskatchewan


In recent decades, the field of phenomics has lagged behind the advances in genomics, which have become increasingly high-throughput and low-cost. In comparison, manually collected phenotypes are often time-consuming, labor intensive, and more costly to obtain. The development of high-throughput phenotyping platforms (HTPP) are bridging these gaps and enabling improved spatial and temporal resolution for researchers. We used imagery from unoccupied aerial vehicles (UAV) flown over multiple site years in Saskatchewan and Italy to gather data for crop height, area and volume in a lentil diversity panel. We found high correlations for our UAV-derived traits (height & volume) with our manually collected phenotypes (height & biomass). In addition, the high-throughput nature of the UAV allowed for the collection of time-series data which enabled the modelling of growth curves for volume, height and area, which would be impractical under traditional phenotyping procedures given the large population grown in multiple environments. Principal component analysis and hierarchical clustering revealed differential growth strategies amongst our diverse lentil population across contrasting environments. Our study demonstrates the potential for HTPP to obtain data that traditionally require destructive sampling, e.g., volume as a proxy for vegetative biomass, and improve the temporal quality of phenotype data enabling researchers to take their analysis beyond single time points, e.g., model growth curves. In addition, performing our analysis on data from contrasting environments, i.e., Saskatchewan and Italy, has helped elucidate optimal adaptation with regard to growth strategies in lentils.
03 Oct 2022Submitted to NAPPN 2023 Abstracts
04 Oct 2022Published in NAPPN 2023 Abstracts