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Tomato detection based on modified YOLOv3 framework
Fruit detection forms a vital part of the robotic harvesting platform. However, uneven environment conditions, such as branch and leaf occlusion, illumination variation, clusters of tomatoes, shading, and so on, have made fruit detection very challenging. In order to solve these problems, a modified...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809275/ https://www.ncbi.nlm.nih.gov/pubmed/33446897 http://dx.doi.org/10.1038/s41598-021-81216-5 |
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author | Lawal, Mubashiru Olarewaju |
author_facet | Lawal, Mubashiru Olarewaju |
author_sort | Lawal, Mubashiru Olarewaju |
collection | PubMed |
description | Fruit detection forms a vital part of the robotic harvesting platform. However, uneven environment conditions, such as branch and leaf occlusion, illumination variation, clusters of tomatoes, shading, and so on, have made fruit detection very challenging. In order to solve these problems, a modified YOLOv3 model called YOLO-Tomato models were adopted to detect tomatoes in complex environmental conditions. With the application of label what you see approach, densely architecture incorporation, spatial pyramid pooling and Mish function activation to the modified YOLOv3 model, the YOLO-Tomato models: YOLO-Tomato-A at AP 98.3% with detection time 48 ms, YOLO-Tomato-B at AP 99.3% with detection time 44 ms, and YOLO-Tomato-C at AP 99.5% with detection time 52 ms, performed better than other state-of-the-art methods. |
format | Online Article Text |
id | pubmed-7809275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78092752021-01-15 Tomato detection based on modified YOLOv3 framework Lawal, Mubashiru Olarewaju Sci Rep Article Fruit detection forms a vital part of the robotic harvesting platform. However, uneven environment conditions, such as branch and leaf occlusion, illumination variation, clusters of tomatoes, shading, and so on, have made fruit detection very challenging. In order to solve these problems, a modified YOLOv3 model called YOLO-Tomato models were adopted to detect tomatoes in complex environmental conditions. With the application of label what you see approach, densely architecture incorporation, spatial pyramid pooling and Mish function activation to the modified YOLOv3 model, the YOLO-Tomato models: YOLO-Tomato-A at AP 98.3% with detection time 48 ms, YOLO-Tomato-B at AP 99.3% with detection time 44 ms, and YOLO-Tomato-C at AP 99.5% with detection time 52 ms, performed better than other state-of-the-art methods. Nature Publishing Group UK 2021-01-14 /pmc/articles/PMC7809275/ /pubmed/33446897 http://dx.doi.org/10.1038/s41598-021-81216-5 Text en © The Author(s) 2021 Open Access This 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/. |
spellingShingle | Article Lawal, Mubashiru Olarewaju Tomato detection based on modified YOLOv3 framework |
title | Tomato detection based on modified YOLOv3 framework |
title_full | Tomato detection based on modified YOLOv3 framework |
title_fullStr | Tomato detection based on modified YOLOv3 framework |
title_full_unstemmed | Tomato detection based on modified YOLOv3 framework |
title_short | Tomato detection based on modified YOLOv3 framework |
title_sort | tomato detection based on modified yolov3 framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809275/ https://www.ncbi.nlm.nih.gov/pubmed/33446897 http://dx.doi.org/10.1038/s41598-021-81216-5 |
work_keys_str_mv | AT lawalmubashiruolarewaju tomatodetectionbasedonmodifiedyolov3framework |