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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/11/2211 |
| _version_ | 1797511596707151872 |
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| author | Dasom Seo Byeong-Hyo Cho Kyoung-Chul Kim |
| author_facet | Dasom Seo Byeong-Hyo Cho Kyoung-Chul Kim |
| author_sort | Dasom Seo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:47:30Z |
| format | Article |
| id | doaj.art-d518360db5c0486da3eb63dd32e2bd5b |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T05:47:30Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-d518360db5c0486da3eb63dd32e2bd5b2023-11-22T22:02:10ZengMDPI AGAgronomy2073-43952021-10-011111221110.3390/agronomy11112211Development of Monitoring Robot System for Tomato Fruits in Hydroponic GreenhousesDasom Seo0Byeong-Hyo Cho1Kyoung-Chul Kim2Department of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju 54875, KoreaDepartment of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju 54875, KoreaDepartment of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju 54875, KoreaCrop 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.https://www.mdpi.com/2073-4395/11/11/2211deep learninghydroponic greenhousematurity levelsmonitoring robotobject detection |
| spellingShingle | Dasom Seo Byeong-Hyo Cho Kyoung-Chul Kim Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses Agronomy deep learning hydroponic greenhouse maturity levels monitoring robot object detection |
| title | 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_short | Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses |
| title_sort | development of monitoring robot system for tomato fruits in hydroponic greenhouses |
| topic | deep learning hydroponic greenhouse maturity levels monitoring robot object detection |
| url | https://www.mdpi.com/2073-4395/11/11/2211 |
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