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Tiller estimation method using deep neural networks
This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880423/ https://www.ncbi.nlm.nih.gov/pubmed/36714728 http://dx.doi.org/10.3389/fpls.2022.1016507 |
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author | Kinose, Rikuya Utsumi, Yuzuko Iwamura, Masakazu Kise, Koichi |
author_facet | Kinose, Rikuya Utsumi, Yuzuko Iwamura, Masakazu Kise, Koichi |
author_sort | Kinose, Rikuya |
collection | PubMed |
description | This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so an automated approach is necessary for high-throughput phenotyping. Conventional methods use heuristic features to estimate the tiller number. Based on the successful application of DNNs in the field of computer vision, the use of DNN-based features instead of heuristic features is expected to improve the estimation accuracy. However, as DNNs generally require large volumes of data for training, it is difficult to apply them to estimation problems for which large training datasets are unavailable. In this paper, we use two strategies to overcome the problem of insufficient training data: the use of a pretrained DNN model and the use of pretext tasks for learning the feature representation. We extract features using the resulting DNNs and estimate the tiller numbers through a regression technique. We conducted experiments using side-view whole plant images taken with plan backgroud. The experimental results show that the proposed methods using a pretrained model and specific pretext tasks achieve better performance than the conventional method. |
format | Online Article Text |
id | pubmed-9880423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98804232023-01-28 Tiller estimation method using deep neural networks Kinose, Rikuya Utsumi, Yuzuko Iwamura, Masakazu Kise, Koichi Front Plant Sci Plant Science This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so an automated approach is necessary for high-throughput phenotyping. Conventional methods use heuristic features to estimate the tiller number. Based on the successful application of DNNs in the field of computer vision, the use of DNN-based features instead of heuristic features is expected to improve the estimation accuracy. However, as DNNs generally require large volumes of data for training, it is difficult to apply them to estimation problems for which large training datasets are unavailable. In this paper, we use two strategies to overcome the problem of insufficient training data: the use of a pretrained DNN model and the use of pretext tasks for learning the feature representation. We extract features using the resulting DNNs and estimate the tiller numbers through a regression technique. We conducted experiments using side-view whole plant images taken with plan backgroud. The experimental results show that the proposed methods using a pretrained model and specific pretext tasks achieve better performance than the conventional method. Frontiers Media S.A. 2023-01-13 /pmc/articles/PMC9880423/ /pubmed/36714728 http://dx.doi.org/10.3389/fpls.2022.1016507 Text en Copyright © 2023 Kinose, Utsumi, Iwamura and Kise 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 Kinose, Rikuya Utsumi, Yuzuko Iwamura, Masakazu Kise, Koichi Tiller estimation method using deep neural networks |
title | Tiller estimation method using deep neural networks |
title_full | Tiller estimation method using deep neural networks |
title_fullStr | Tiller estimation method using deep neural networks |
title_full_unstemmed | Tiller estimation method using deep neural networks |
title_short | Tiller estimation method using deep neural networks |
title_sort | tiller estimation method using deep neural networks |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880423/ https://www.ncbi.nlm.nih.gov/pubmed/36714728 http://dx.doi.org/10.3389/fpls.2022.1016507 |
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