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The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations
Grain yield (GY) prediction based on non-destructive UAV-based spectral sensing could make screening of large field trials more efficient and objective. However, the transfer of models remains challenging, and is affected by location, year-dependent weather conditions and measurement dates. Therefor...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145428/ https://www.ncbi.nlm.nih.gov/pubmed/37112518 http://dx.doi.org/10.3390/s23084177 |
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author | Prey, Lukas Ramgraber, Ludwig Seidl-Schulz, Johannes Hanemann, Anja Noack, Patrick Ole |
author_facet | Prey, Lukas Ramgraber, Ludwig Seidl-Schulz, Johannes Hanemann, Anja Noack, Patrick Ole |
author_sort | Prey, Lukas |
collection | PubMed |
description | Grain yield (GY) prediction based on non-destructive UAV-based spectral sensing could make screening of large field trials more efficient and objective. However, the transfer of models remains challenging, and is affected by location, year-dependent weather conditions and measurement dates. Therefore, this study evaluates GY modelling across years and locations, considering the effect of measurement dates within years. Based on a previous study, we used a normalized difference red edge (NDRE1) index with PLS (partial least squares) regression, trained and tested with the data of individual dates and date combinations, respectively. While strong differences in model performance were observed between test datasets, i.e., different trials, as well as between measurement dates, the effect of the train datasets was comparably small. Generally, within-trials models achieved better predictions (max. R(2) = 0.27–0.81), but R(2)-values for the best across-trials models were lower only by 0.03–0.13. Within train and test datasets, measurement dates had a strong influence on model performance. While measurements during flowering and early milk ripeness were confirmed for within- and across-trials models, later dates were less useful for across-trials models. For most test sets, multi-date models revealed to improve predictions compared to individual-date models. |
format | Online Article Text |
id | pubmed-10145428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101454282023-04-29 The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations Prey, Lukas Ramgraber, Ludwig Seidl-Schulz, Johannes Hanemann, Anja Noack, Patrick Ole Sensors (Basel) Article Grain yield (GY) prediction based on non-destructive UAV-based spectral sensing could make screening of large field trials more efficient and objective. However, the transfer of models remains challenging, and is affected by location, year-dependent weather conditions and measurement dates. Therefore, this study evaluates GY modelling across years and locations, considering the effect of measurement dates within years. Based on a previous study, we used a normalized difference red edge (NDRE1) index with PLS (partial least squares) regression, trained and tested with the data of individual dates and date combinations, respectively. While strong differences in model performance were observed between test datasets, i.e., different trials, as well as between measurement dates, the effect of the train datasets was comparably small. Generally, within-trials models achieved better predictions (max. R(2) = 0.27–0.81), but R(2)-values for the best across-trials models were lower only by 0.03–0.13. Within train and test datasets, measurement dates had a strong influence on model performance. While measurements during flowering and early milk ripeness were confirmed for within- and across-trials models, later dates were less useful for across-trials models. For most test sets, multi-date models revealed to improve predictions compared to individual-date models. MDPI 2023-04-21 /pmc/articles/PMC10145428/ /pubmed/37112518 http://dx.doi.org/10.3390/s23084177 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Prey, Lukas Ramgraber, Ludwig Seidl-Schulz, Johannes Hanemann, Anja Noack, Patrick Ole The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations |
title | The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations |
title_full | The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations |
title_fullStr | The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations |
title_full_unstemmed | The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations |
title_short | The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations |
title_sort | transferability of spectral grain yield prediction in wheat breeding across years and trial locations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145428/ https://www.ncbi.nlm.nih.gov/pubmed/37112518 http://dx.doi.org/10.3390/s23084177 |
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