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High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model
Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machi...
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/PMC9201825/ https://www.ncbi.nlm.nih.gov/pubmed/35720576 http://dx.doi.org/10.3389/fpls.2022.787852 |
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author | Zhang, Yan Li, Manzhou Ma, Xiaoxiao Wu, Xiaotong Wang, Yaojun |
author_facet | Zhang, Yan Li, Manzhou Ma, Xiaoxiao Wu, Xiaotong Wang, Yaojun |
author_sort | Zhang, Yan |
collection | PubMed |
description | Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a mAP of 0.688. In addition, an iOS-based intelligent wheat head counting mobile app was created, which could calculate the number of wheat heads in images shot in an agricultural environment in less than a second. |
format | Online Article Text |
id | pubmed-9201825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92018252022-06-17 High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model Zhang, Yan Li, Manzhou Ma, Xiaoxiao Wu, Xiaotong Wang, Yaojun Front Plant Sci Plant Science Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a mAP of 0.688. In addition, an iOS-based intelligent wheat head counting mobile app was created, which could calculate the number of wheat heads in images shot in an agricultural environment in less than a second. Frontiers Media S.A. 2022-06-02 /pmc/articles/PMC9201825/ /pubmed/35720576 http://dx.doi.org/10.3389/fpls.2022.787852 Text en Copyright © 2022 Zhang, Li, Ma, Wu and Wang. 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, Yan Li, Manzhou Ma, Xiaoxiao Wu, Xiaotong Wang, Yaojun High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model |
title | High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model |
title_full | High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model |
title_fullStr | High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model |
title_full_unstemmed | High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model |
title_short | High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model |
title_sort | high-precision wheat head detection model based on one-stage network and gan model |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201825/ https://www.ncbi.nlm.nih.gov/pubmed/35720576 http://dx.doi.org/10.3389/fpls.2022.787852 |
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