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Remote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance: Effects of growing temperature and vernalization.

Plant science : an international journal of experimental plant biology · 21 Sept 2019 · 10.1016/j.plantsci.2019.110281

Abstract

This study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs. A set of 38 (winter and facultative) wheat cultivars were planted in Valladolid (Spain) under irrigation and two contrasting planting dates: normal (late autumn), and late (late winter). The late plating trial exhibited a 1.5 °C increase in average crop temperature. Measurements with different remote sensing techniques were performed at heading and grain filling, as well as carbon isotope composition (δ 13 C) and nitrogen content analysis. Multispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY) across the whole set of genotypes in the normal compared with the late planting, with indices (such as the RGB indices Hue, a* and the spectral indices NDVI, EVI and CCI) measured at grain filling performing the best. Aerially assessed remote sensing indices only performed better than ground-acquired ones at heading. Nitrogen content and δ 13 C correlated with GY at both planting dates. Correlations within winter and facultative genotypes were much weaker, particularly in the facultative subset. For both planting dates, the best GY prediction models were achieved when combining remote sensing indices with δ 13 C and nitrogen of mature grains. Implications for phenotyping in the context of increasing temperatures are further discussed.

Plant phenotyping relevance

小麦収量を対象に、マルチスペクトル・RGB・熱赤外リモートセンシングと同位体指標を比較し、収量予測性能を評価することが中心であり、表現型取得・推定手法の検証に該当する。

titleRemote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance
abstractThis study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs.
abstractMultispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY)
abstractthe best GY prediction models were achieved when combining remote sensing indices with δ 13 C and nitrogen of mature grains.

Code and data availability

The paper used the authors' MosaicTool software (a FIJI plugin) to crop and process UAV RGB/thermal/multispectral plot images and compute vegetation indices for this wheat phenotyping study. MosaicTool is publicly available via the authors' GitLab repository and project page, both listed in the article text and in the,

Codepublic

later overlaps up to 30 images (with at least 80% ro overlap) and removes UAV flight effects to produce accurate ortho-mosaics. Afterwards, regions of interest (plots) were cropped and processed using the MosaicTool software (Prof. Shawn C. Kefauver, https://integrativecropecophysiology.com/software-development/mosaictool/, -p https://gitlab.com/sckefauver/MosaicTool/, University of Barcelona, Barcelona, Spain) integrated as a plugin for the open source image analysis platform FIJI (Fiji is Just ImageJ; http://fiji.sc/Fiji) [40]. re Extracted RGB vegetation indices collected from both ground and aerial platforms were obtained using an updated version of the original Breedpix 2.0 software [41

Open resource ↗sckefauver/MosaicTool · pdf-layout-page:7 lines:1-78

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