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High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass

Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments. A high spatial resolution RGB camera was u...

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Autores principales: Jin, Xiuliang, Madec, Simon, Dutartre, Dan, de Solan, Benoit, Comar, Alexis, Baret, Frédéric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706336/
https://www.ncbi.nlm.nih.gov/pubmed/33313528
http://dx.doi.org/10.34133/2019/4820305
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author Jin, Xiuliang
Madec, Simon
Dutartre, Dan
de Solan, Benoit
Comar, Alexis
Baret, Frédéric
author_facet Jin, Xiuliang
Madec, Simon
Dutartre, Dan
de Solan, Benoit
Comar, Alexis
Baret, Frédéric
author_sort Jin, Xiuliang
collection PubMed
description Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments. A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest. It provided a ground spatial resolution better than 0.2 mm. A Faster Regional Convolutional Neural Network (Faster-RCNN) deep-learning model was first trained to identify the stems cross section. Results showed that the identification provided precision and recall close to 95%. Further, the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7% and robustness across the two experimental sites. The estimated stem density was also compared with the ear density measured in the field with traditional methods. A very high correlation was found with almost no bias, indicating that the stem density could be a good proxy of the ear density. The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher (80%) than that of the ear density (78%). The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section. Results show that the stem diameters follow a gamma distribution over each microplot with an average diameter close to 2.0 mm. Finally, the biovolume computed as the product of the average stem diameter, the stem density, and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%. Possible limitations of the findings and future applications are finally discussed.
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spelling pubmed-77063362020-12-10 High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass Jin, Xiuliang Madec, Simon Dutartre, Dan de Solan, Benoit Comar, Alexis Baret, Frédéric Plant Phenomics Research Article Total above-ground biomass at harvest and ear density are two important traits that characterize wheat genotypes. Two experiments were carried out in two different sites where several genotypes were grown under contrasted irrigation and nitrogen treatments. A high spatial resolution RGB camera was used to capture the residual stems standing straight after the cutting by the combine machine during harvest. It provided a ground spatial resolution better than 0.2 mm. A Faster Regional Convolutional Neural Network (Faster-RCNN) deep-learning model was first trained to identify the stems cross section. Results showed that the identification provided precision and recall close to 95%. Further, the balance between precision and recall allowed getting accurate estimates of the stem density with a relative RMSE close to 7% and robustness across the two experimental sites. The estimated stem density was also compared with the ear density measured in the field with traditional methods. A very high correlation was found with almost no bias, indicating that the stem density could be a good proxy of the ear density. The heritability/repeatability evaluated over 16 genotypes in one of the two experiments was slightly higher (80%) than that of the ear density (78%). The diameter of each stem was computed from the profile of gray values in the extracts of the stem cross section. Results show that the stem diameters follow a gamma distribution over each microplot with an average diameter close to 2.0 mm. Finally, the biovolume computed as the product of the average stem diameter, the stem density, and plant height is closely related to the above-ground biomass at harvest with a relative RMSE of 6%. Possible limitations of the findings and future applications are finally discussed. AAAS 2019-05-30 /pmc/articles/PMC7706336/ /pubmed/33313528 http://dx.doi.org/10.34133/2019/4820305 Text en Copyright © 2019 Xiuliang Jin et al. https://creativecommons.org/licenses/by/4.0/ Exclusive licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0).
spellingShingle Research Article
Jin, Xiuliang
Madec, Simon
Dutartre, Dan
de Solan, Benoit
Comar, Alexis
Baret, Frédéric
High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass
title High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass
title_full High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass
title_fullStr High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass
title_full_unstemmed High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass
title_short High-Throughput Measurements of Stem Characteristics to Estimate Ear Density and Above-Ground Biomass
title_sort high-throughput measurements of stem characteristics to estimate ear density and above-ground biomass
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706336/
https://www.ncbi.nlm.nih.gov/pubmed/33313528
http://dx.doi.org/10.34133/2019/4820305
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