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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-8787134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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