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YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings
Accurate identification of crop varieties is an important aspect of smart agriculture, which is not only essential for the management of later crop differences, but also has a significant effect on unmanned operations in planting scenarios such as facility greenhouses. In this study, five kinds of l...
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/PMC8909383/ https://www.ncbi.nlm.nih.gov/pubmed/35283870 http://dx.doi.org/10.3389/fpls.2022.806878 |
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author | Zhang, Pan Li, Daoliang |
author_facet | Zhang, Pan Li, Daoliang |
author_sort | Zhang, Pan |
collection | PubMed |
description | Accurate identification of crop varieties is an important aspect of smart agriculture, which is not only essential for the management of later crop differences, but also has a significant effect on unmanned operations in planting scenarios such as facility greenhouses. In this study, five kinds of lettuce under the cultivation conditions of greenhouses were used as the research object, and a classification model of lettuce varieties with multiple growth stages was established. First of all, we used the-state-of-the-art method VOLO-D1 to establish a variety classification model for the 7 growth stages of the entire growth process. The results found that the performance of the lettuce variety classification model in the SP stage needs to be improved, but the classification effect of the model at other stages is close to 100%; Secondly, based on the challenges of the SP stage dataset, we combined the advantages of the target detection mechanism and the target classification mechanism, innovatively proposed a new method of variety identification for the SP stage, called YOLO-VOLO-LS. Finally, we used this method to model and analyze the classification of lettuce varieties in the SP stage. The result shows that the method can achieve excellent results of 95.961, 93.452, 96.059, 96.014, 96.039 in Val-acc, Test-acc, Recall, Precision, F1-score, respectively. Therefore, the method proposed in this study has a certain reference value for the accurate identification of varieties in the early growth stage of crops. |
format | Online Article Text |
id | pubmed-8909383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89093832022-03-11 YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings Zhang, Pan Li, Daoliang Front Plant Sci Plant Science Accurate identification of crop varieties is an important aspect of smart agriculture, which is not only essential for the management of later crop differences, but also has a significant effect on unmanned operations in planting scenarios such as facility greenhouses. In this study, five kinds of lettuce under the cultivation conditions of greenhouses were used as the research object, and a classification model of lettuce varieties with multiple growth stages was established. First of all, we used the-state-of-the-art method VOLO-D1 to establish a variety classification model for the 7 growth stages of the entire growth process. The results found that the performance of the lettuce variety classification model in the SP stage needs to be improved, but the classification effect of the model at other stages is close to 100%; Secondly, based on the challenges of the SP stage dataset, we combined the advantages of the target detection mechanism and the target classification mechanism, innovatively proposed a new method of variety identification for the SP stage, called YOLO-VOLO-LS. Finally, we used this method to model and analyze the classification of lettuce varieties in the SP stage. The result shows that the method can achieve excellent results of 95.961, 93.452, 96.059, 96.014, 96.039 in Val-acc, Test-acc, Recall, Precision, F1-score, respectively. Therefore, the method proposed in this study has a certain reference value for the accurate identification of varieties in the early growth stage of crops. Frontiers Media S.A. 2022-02-24 /pmc/articles/PMC8909383/ /pubmed/35283870 http://dx.doi.org/10.3389/fpls.2022.806878 Text en Copyright © 2022 Zhang and Li. 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 Zhang, Pan Li, Daoliang YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings |
title | YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings |
title_full | YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings |
title_fullStr | YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings |
title_full_unstemmed | YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings |
title_short | YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings |
title_sort | yolo-volo-ls: a novel method for variety identification of early lettuce seedlings |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8909383/ https://www.ncbi.nlm.nih.gov/pubmed/35283870 http://dx.doi.org/10.3389/fpls.2022.806878 |
work_keys_str_mv | AT zhangpan yolovololsanovelmethodforvarietyidentificationofearlylettuceseedlings AT lidaoliang yolovololsanovelmethodforvarietyidentificationofearlylettuceseedlings |