Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images

Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Objec...

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Autores principales:	Motohisa Fukuda, Takashi Okuno, Shinya Yuki
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning U-Net image segmentation central object fruit pear Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/7688cef7c3d34efaa9027147469c2408
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id	oai:doaj.org-article:7688cef7c3d34efaa9027147469c2408
record_format	dspace
spelling	oai:doaj.org-article:7688cef7c3d34efaa9027147469c24082021-11-11T19:02:29ZCentral Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images10.3390/s212169991424-8220https://doaj.org/article/7688cef7c3d34efaa9027147469c24082021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6999https://doaj.org/toc/1424-8220Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields.Motohisa FukudaTakashi OkunoShinya YukiMDPI AGarticledeep learningU-Netimage segmentationcentral objectfruitpearChemical technologyTP1-1185ENSensors, Vol 21, Iss 6999, p 6999 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning U-Net image segmentation central object fruit pear Chemical technology TP1-1185
spellingShingle	deep learning U-Net image segmentation central object fruit pear Chemical technology TP1-1185 Motohisa Fukuda Takashi Okuno Shinya Yuki Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images
description	Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields.
format	article
author	Motohisa Fukuda Takashi Okuno Shinya Yuki
author_facet	Motohisa Fukuda Takashi Okuno Shinya Yuki
author_sort	Motohisa Fukuda
title	Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images
title_short	Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images
title_full	Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images
title_fullStr	Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images
title_full_unstemmed	Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images
title_sort	central object segmentation by deep learning to continuously monitor fruit growth through rgb images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7688cef7c3d34efaa9027147469c2408
work_keys_str_mv	AT motohisafukuda centralobjectsegmentationbydeeplearningtocontinuouslymonitorfruitgrowththroughrgbimages AT takashiokuno centralobjectsegmentationbydeeplearningtocontinuouslymonitorfruitgrowththroughrgbimages AT shinyayuki centralobjectsegmentationbydeeplearningtocontinuouslymonitorfruitgrowththroughrgbimages
_version_	1718431652311465984

Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images

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