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Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

BACKGROUND: Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials i...

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Autores principales: Li, Lili, Qiao, Jiangwei, Yao, Jian, Li, Jie, Li, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756653/
https://www.ncbi.nlm.nih.gov/pubmed/35027060
http://dx.doi.org/10.1186/s13007-022-00838-6
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author Li, Lili
Qiao, Jiangwei
Yao, Jian
Li, Jie
Li, Li
author_facet Li, Lili
Qiao, Jiangwei
Yao, Jian
Li, Jie
Li, Li
author_sort Li, Lili
collection PubMed
description BACKGROUND: Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. METHODS: To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. RESULT AND CONCLUSION: The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33[Formula: see text] ) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.
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spelling pubmed-87566532022-01-18 Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning Li, Lili Qiao, Jiangwei Yao, Jian Li, Jie Li, Li Plant Methods Research BACKGROUND: Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. METHODS: To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. RESULT AND CONCLUSION: The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33[Formula: see text] ) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning. BioMed Central 2022-01-13 /pmc/articles/PMC8756653/ /pubmed/35027060 http://dx.doi.org/10.1186/s13007-022-00838-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Lili
Qiao, Jiangwei
Yao, Jian
Li, Jie
Li, Li
Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning
title Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning
title_full Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning
title_fullStr Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning
title_full_unstemmed Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning
title_short Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning
title_sort automatic freezing-tolerant rapeseed material recognition using uav images and deep learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756653/
https://www.ncbi.nlm.nih.gov/pubmed/35027060
http://dx.doi.org/10.1186/s13007-022-00838-6
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