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Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards

Evaluation of the legume proportion in grass-legume mixed swards is necessary for breeding and for cultivation research of forage. For objective and time-efficient estimation of legume proportion, convolutional neural network (CNN) models were trained by fine-tuning the GoogLeNet to estimate the cov...

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Autores principales: Fujiwara, Ryo, Nashida, Hiroyuki, Fukushima, Midori, Suzuki, Naoya, Sato, Hiroko, Sanada, Yasuharu, Akiyama, Yukio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787134/
https://www.ncbi.nlm.nih.gov/pubmed/35087545
http://dx.doi.org/10.3389/fpls.2021.763479
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author Fujiwara, Ryo
Nashida, Hiroyuki
Fukushima, Midori
Suzuki, Naoya
Sato, Hiroko
Sanada, Yasuharu
Akiyama, Yukio
author_facet Fujiwara, Ryo
Nashida, Hiroyuki
Fukushima, Midori
Suzuki, Naoya
Sato, Hiroko
Sanada, Yasuharu
Akiyama, Yukio
author_sort Fujiwara, Ryo
collection PubMed
description Evaluation of the legume proportion in grass-legume mixed swards is necessary for breeding and for cultivation research of forage. For objective and time-efficient estimation of legume proportion, convolutional neural network (CNN) models were trained by fine-tuning the GoogLeNet to estimate the coverage of timothy (TY), white clover (WC), and background (Bg) on the unmanned aerial vehicle-based images. The accuracies of the CNN models trained on different datasets were compared using the mean bias error and the mean average error. The models predicted the coverage with small errors when the plots in the training datasets were similar to the target plots in terms of coverage rate. The models that are trained on datasets of multiple plots had smaller errors than those trained on datasets of a single plot. The CNN models estimated the WC coverage more precisely than they did to the TY and the Bg coverages. The correlation coefficients (r) of the measured coverage for aerial images vs. estimated coverage were 0.92–0.96, whereas those of the scored coverage by a breeder vs. estimated coverage were 0.76–0.93. These results indicate that CNN models are helpful in effectively estimating the legume coverage.
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spelling pubmed-87871342022-01-26 Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards Fujiwara, Ryo Nashida, Hiroyuki Fukushima, Midori Suzuki, Naoya Sato, Hiroko Sanada, Yasuharu Akiyama, Yukio Front Plant Sci Plant Science Evaluation of the legume proportion in grass-legume mixed swards is necessary for breeding and for cultivation research of forage. For objective and time-efficient estimation of legume proportion, convolutional neural network (CNN) models were trained by fine-tuning the GoogLeNet to estimate the coverage of timothy (TY), white clover (WC), and background (Bg) on the unmanned aerial vehicle-based images. The accuracies of the CNN models trained on different datasets were compared using the mean bias error and the mean average error. The models predicted the coverage with small errors when the plots in the training datasets were similar to the target plots in terms of coverage rate. The models that are trained on datasets of multiple plots had smaller errors than those trained on datasets of a single plot. The CNN models estimated the WC coverage more precisely than they did to the TY and the Bg coverages. The correlation coefficients (r) of the measured coverage for aerial images vs. estimated coverage were 0.92–0.96, whereas those of the scored coverage by a breeder vs. estimated coverage were 0.76–0.93. These results indicate that CNN models are helpful in effectively estimating the legume coverage. Frontiers Media S.A. 2022-01-11 /pmc/articles/PMC8787134/ /pubmed/35087545 http://dx.doi.org/10.3389/fpls.2021.763479 Text en Copyright © 2022 Fujiwara, Nashida, Fukushima, Suzuki, Sato, Sanada and Akiyama. 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
Fujiwara, Ryo
Nashida, Hiroyuki
Fukushima, Midori
Suzuki, Naoya
Sato, Hiroko
Sanada, Yasuharu
Akiyama, Yukio
Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards
title Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards
title_full Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards
title_fullStr Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards
title_full_unstemmed Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards
title_short Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards
title_sort convolutional neural network models help effectively estimate legume coverage in grass-legume mixed swards
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787134/
https://www.ncbi.nlm.nih.gov/pubmed/35087545
http://dx.doi.org/10.3389/fpls.2021.763479
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