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Tattaris M, Reynolds MP, Chapman SC. A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding. Front Plant Sci. 2016;7:1131doi: 10.3389/fpls.2016.01131.
Tattaris, M., Reynolds, M. P., & Chapman, S. C. (2016). A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding. Frontiers in plant science, 71131. https://doi.org/10.3389/fpls.2016.01131
Tattaris, Maria, et al. "A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding." Frontiers in plant science vol. 7 (2016): 1131. doi: https://doi.org/10.3389/fpls.2016.01131
Tattaris M, Reynolds MP, Chapman SC. A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding. Front Plant Sci. 2016 Aug 03;7:1131. doi: 10.3389/fpls.2016.01131. eCollection 2016. PMID: 27536304; PMCID: PMC4971441.
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Front Plant Sci. 2016 Aug 03;7:1131. doi: 10.3389/fpls.2016.01131. eCollection 2016.

A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding.

Frontiers in plant science

Maria Tattaris, Matthew P Reynolds, Scott C Chapman

Affiliations

  1. International Maize and Wheat Improvement Center Texcoco, Mexico.
  2. CSIRO Agriculture, Queensland Bioscience Precinct Queensland, QLD, Australia.

PMID: 27536304 PMCID: PMC4971441 DOI: 10.3389/fpls.2016.01131

Abstract

Remote sensing (RS) of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle), with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT) and a vegetation index (NDVI), to determine the most viable approaches for large scale crop genetic improvement. The UAV-based platform achieves plot-level resolution while measuring several hundred plots in one mission via high-resolution thermal and multispectral imagery measured at altitudes of 30-100 m. The satellite measures multispectral imagery from an altitude of 770 km. Information was compared with proximal measurements using IR thermometers and an NDVI sensor at a distance of 0.5-1 m above plots. For robust comparisons, CT and NDVI were assessed on panels of elite cultivars under irrigated and drought conditions, in different thermal regimes, and on un-adapted genetic resources under water deficit. Correlations between airborne data and yield/biomass at maturity were generally higher than equivalent proximal correlations. NDVI was derived from high-resolution satellite imagery for only larger sized plots (8.5 × 2.4 m) due to restricted pixel density. Results support use of UAV-based RS techniques for high-throughput phenotyping for both precision and efficiency.

Keywords: UAV; airborne imagery; high-throughput phenotyping; indices; multispectral; thermal

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