Tomato detection based on modified YOLOv3 framework

Abstract 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...

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Autor principal:	Mubashiru Olarewaju Lawal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7b11b5841271447a86068d5cee23a7c9
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spelling	oai:doaj.org-article:7b11b5841271447a86068d5cee23a7c92021-12-02T14:12:08ZTomato detection based on modified YOLOv3 framework10.1038/s41598-021-81216-52045-2322https://doaj.org/article/7b11b5841271447a86068d5cee23a7c92021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81216-5https://doaj.org/toc/2045-2322Abstract 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.Mubashiru Olarewaju LawalNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mubashiru Olarewaju Lawal Tomato detection based on modified YOLOv3 framework
description	Abstract 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	article
author	Mubashiru Olarewaju Lawal
author_facet	Mubashiru Olarewaju Lawal
author_sort	Mubashiru Olarewaju Lawal
title	Tomato detection based on modified YOLOv3 framework
title_short	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_sort	tomato detection based on modified yolov3 framework
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7b11b5841271447a86068d5cee23a7c9
work_keys_str_mv	AT mubashiruolarewajulawal tomatodetectionbasedonmodifiedyolov3framework
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Tomato detection based on modified YOLOv3 framework

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