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Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction
Canopy-intercepted light, or photosynthetically active radiation, is fundamentally crucial for quantifying crop biomass development and yield potential. Fractional photosynthetically active radiation (PAR) (fPAR) is conventionally obtained by measuring the PAR both below and above the canopy using a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427806/ https://www.ncbi.nlm.nih.gov/pubmed/34512697 http://dx.doi.org/10.3389/fpls.2021.715361 |
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author | Zhang, Xin Pourreza, Alireza Cheung, Kyle H. Zuniga-Ramirez, German Lampinen, Bruce D. Shackel, Kenneth A. |
author_facet | Zhang, Xin Pourreza, Alireza Cheung, Kyle H. Zuniga-Ramirez, German Lampinen, Bruce D. Shackel, Kenneth A. |
author_sort | Zhang, Xin |
collection | PubMed |
description | Canopy-intercepted light, or photosynthetically active radiation, is fundamentally crucial for quantifying crop biomass development and yield potential. Fractional photosynthetically active radiation (PAR) (fPAR) is conventionally obtained by measuring the PAR both below and above the canopy using a mobile lightbar platform to predict the potential yield of nut crops. This study proposed a feasible and low-cost method for accurately estimating the canopy fPAR using aerial photogrammetry-based canopy three-dimensional models. We tested up to eight different varieties in three experimental almond orchards, including California's leading variety of ‘Nonpareil’. To extract various canopy profile features, such as canopy cover and canopy volume index, we developed a complete data collection and processing pipeline called Virtual Orchard (VO) in Python environment. Canopy fPAR estimated by VO throughout the season was compared against midday canopy fPAR measured by a mobile lightbar platform in midseason, achieving a strong correlation (R(2)) of 0.96. A low root mean square error (RMSE) of 2% for ‘Nonpareil’. Furthermore, we developed regression models for predicting actual almond yield using both measures, where VO estimation of canopy fPAR, as a stronger indicator, achieved a much better prediction (R(2) = 0.84 and RMSE = 195 lb acre(−1)) than the lightbar (R(2) = 0.70 and RMSE = 266 lb acre(−1)) for ‘Nonpareil’. Eight different new models for estimating potential yield were also developed using temporal analysis from May to August in 2019 by adjusting the ratio between fPAR and dry kernel yield previously found using a lightbar. Finally, we compared the two measures at two different spatial precision levels: per-row and per-block. fPAR estimated by VO at the per-tree level was also assessed. Results showed that VO estimated canopy fPAR performed better at each precision level than lightbar with up to 0.13 higher R(2). The findings in this study serve as a fundamental link between aerial-based canopy fPAR and the actual yield of almonds. |
format | Online Article Text |
id | pubmed-8427806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84278062021-09-10 Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction Zhang, Xin Pourreza, Alireza Cheung, Kyle H. Zuniga-Ramirez, German Lampinen, Bruce D. Shackel, Kenneth A. Front Plant Sci Plant Science Canopy-intercepted light, or photosynthetically active radiation, is fundamentally crucial for quantifying crop biomass development and yield potential. Fractional photosynthetically active radiation (PAR) (fPAR) is conventionally obtained by measuring the PAR both below and above the canopy using a mobile lightbar platform to predict the potential yield of nut crops. This study proposed a feasible and low-cost method for accurately estimating the canopy fPAR using aerial photogrammetry-based canopy three-dimensional models. We tested up to eight different varieties in three experimental almond orchards, including California's leading variety of ‘Nonpareil’. To extract various canopy profile features, such as canopy cover and canopy volume index, we developed a complete data collection and processing pipeline called Virtual Orchard (VO) in Python environment. Canopy fPAR estimated by VO throughout the season was compared against midday canopy fPAR measured by a mobile lightbar platform in midseason, achieving a strong correlation (R(2)) of 0.96. A low root mean square error (RMSE) of 2% for ‘Nonpareil’. Furthermore, we developed regression models for predicting actual almond yield using both measures, where VO estimation of canopy fPAR, as a stronger indicator, achieved a much better prediction (R(2) = 0.84 and RMSE = 195 lb acre(−1)) than the lightbar (R(2) = 0.70 and RMSE = 266 lb acre(−1)) for ‘Nonpareil’. Eight different new models for estimating potential yield were also developed using temporal analysis from May to August in 2019 by adjusting the ratio between fPAR and dry kernel yield previously found using a lightbar. Finally, we compared the two measures at two different spatial precision levels: per-row and per-block. fPAR estimated by VO at the per-tree level was also assessed. Results showed that VO estimated canopy fPAR performed better at each precision level than lightbar with up to 0.13 higher R(2). The findings in this study serve as a fundamental link between aerial-based canopy fPAR and the actual yield of almonds. Frontiers Media S.A. 2021-08-26 /pmc/articles/PMC8427806/ /pubmed/34512697 http://dx.doi.org/10.3389/fpls.2021.715361 Text en Copyright © 2021 Zhang, Pourreza, Cheung, Zuniga-Ramirez, Lampinen and Shackel. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Zhang, Xin Pourreza, Alireza Cheung, Kyle H. Zuniga-Ramirez, German Lampinen, Bruce D. Shackel, Kenneth A. Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction |
title | Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction |
title_full | Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction |
title_fullStr | Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction |
title_full_unstemmed | Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction |
title_short | Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction |
title_sort | estimation of fractional photosynthetically active radiation from a canopy 3d model; case study: almond yield prediction |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427806/ https://www.ncbi.nlm.nih.gov/pubmed/34512697 http://dx.doi.org/10.3389/fpls.2021.715361 |
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