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YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting

BACKGROUND: The number of soybean pods is one of the most important indicators of soybean yield, pod counting is crucial for yield estimation, cultivation management, and variety breeding. Counting pods manually is slow and laborious. For crop counting, using object detection network is a common pra...

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Autores principales: Xiang, Shuai, Wang, Siyu, Xu, Mei, Wang, Wenyan, Liu, Weiguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883929/
https://www.ncbi.nlm.nih.gov/pubmed/36709313
http://dx.doi.org/10.1186/s13007-023-00985-4
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author Xiang, Shuai
Wang, Siyu
Xu, Mei
Wang, Wenyan
Liu, Weiguo
author_facet Xiang, Shuai
Wang, Siyu
Xu, Mei
Wang, Wenyan
Liu, Weiguo
author_sort Xiang, Shuai
collection PubMed
description BACKGROUND: The number of soybean pods is one of the most important indicators of soybean yield, pod counting is crucial for yield estimation, cultivation management, and variety breeding. Counting pods manually is slow and laborious. For crop counting, using object detection network is a common practice, but the scattered and overlapped pods make the detection and counting of the pods difficult. RESULTS: We propose an approach that we named YOLO POD, based on the YOLO X framework. On top of YOLO X, we added a block for predicting the number of pods, modified the loss function, thus constructing a multi-task model, and introduced the Convolutional Block Attention Module (CBAM). We achieve accurate identification and counting of pods without reducing the speed of inference. The results showed that the R(2) between the number predicted by YOLO POD and the ground truth reached 0.967, which is improved by 0.049 compared to YOLO X, while the inference time only increased by 0.08 s. Moreover, MAE, MAPE, RMSE are only 4.18, 10.0%, 6.48 respectively, the deviation is very small. CONCLUSIONS: We have achieved the first accurate counting of soybean pods and proposed a new solution for the detection and counting of dense objects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-00985-4.
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spelling pubmed-98839292023-01-29 YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting Xiang, Shuai Wang, Siyu Xu, Mei Wang, Wenyan Liu, Weiguo Plant Methods Research BACKGROUND: The number of soybean pods is one of the most important indicators of soybean yield, pod counting is crucial for yield estimation, cultivation management, and variety breeding. Counting pods manually is slow and laborious. For crop counting, using object detection network is a common practice, but the scattered and overlapped pods make the detection and counting of the pods difficult. RESULTS: We propose an approach that we named YOLO POD, based on the YOLO X framework. On top of YOLO X, we added a block for predicting the number of pods, modified the loss function, thus constructing a multi-task model, and introduced the Convolutional Block Attention Module (CBAM). We achieve accurate identification and counting of pods without reducing the speed of inference. The results showed that the R(2) between the number predicted by YOLO POD and the ground truth reached 0.967, which is improved by 0.049 compared to YOLO X, while the inference time only increased by 0.08 s. Moreover, MAE, MAPE, RMSE are only 4.18, 10.0%, 6.48 respectively, the deviation is very small. CONCLUSIONS: We have achieved the first accurate counting of soybean pods and proposed a new solution for the detection and counting of dense objects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-023-00985-4. BioMed Central 2023-01-28 /pmc/articles/PMC9883929/ /pubmed/36709313 http://dx.doi.org/10.1186/s13007-023-00985-4 Text en © The Author(s) 2023, corrected publication 2023 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
Xiang, Shuai
Wang, Siyu
Xu, Mei
Wang, Wenyan
Liu, Weiguo
YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting
title YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting
title_full YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting
title_fullStr YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting
title_full_unstemmed YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting
title_short YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting
title_sort yolo pod: a fast and accurate multi-task model for dense soybean pod counting
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883929/
https://www.ncbi.nlm.nih.gov/pubmed/36709313
http://dx.doi.org/10.1186/s13007-023-00985-4
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