Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses

Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms...

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Autores principales:	Dasom Seo, Byeong-Hyo Cho, Kyoung-Chul Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning hydroponic greenhouse maturity levels monitoring robot object detection Agriculture S
Acceso en línea:	https://doaj.org/article/d518360db5c0486da3eb63dd32e2bd5b
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id	oai:doaj.org-article:d518360db5c0486da3eb63dd32e2bd5b
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spelling	oai:doaj.org-article:d518360db5c0486da3eb63dd32e2bd5b2021-11-25T16:06:57ZDevelopment of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses10.3390/agronomy111122112073-4395https://doaj.org/article/d518360db5c0486da3eb63dd32e2bd5b2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2211https://doaj.org/toc/2073-4395Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts.Dasom SeoByeong-Hyo ChoKyoung-Chul KimMDPI AGarticledeep learninghydroponic greenhousematurity levelsmonitoring robotobject detectionAgricultureSENAgronomy, Vol 11, Iss 2211, p 2211 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning hydroponic greenhouse maturity levels monitoring robot object detection Agriculture S
spellingShingle	deep learning hydroponic greenhouse maturity levels monitoring robot object detection Agriculture S Dasom Seo Byeong-Hyo Cho Kyoung-Chul Kim Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses
description	Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts.
format	article
author	Dasom Seo Byeong-Hyo Cho Kyoung-Chul Kim
author_facet	Dasom Seo Byeong-Hyo Cho Kyoung-Chul Kim
author_sort	Dasom Seo
title	Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses
title_short	Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses
title_full	Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses
title_fullStr	Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses
title_full_unstemmed	Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses
title_sort	development of monitoring robot system for tomato fruits in hydroponic greenhouses
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d518360db5c0486da3eb63dd32e2bd5b
work_keys_str_mv	AT dasomseo developmentofmonitoringrobotsystemfortomatofruitsinhydroponicgreenhouses AT byeonghyocho developmentofmonitoringrobotsystemfortomatofruitsinhydroponicgreenhouses AT kyoungchulkim developmentofmonitoringrobotsystemfortomatofruitsinhydroponicgreenhouses
_version_	1718413345065795584

Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses

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