Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images
Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5140 |
| _version_ | 1797443870145904640 |
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| author | Truong Thi Huong Giang Tran Quoc Khai Dae-Young Im Young-Jae Ryoo |
| author_facet | Truong Thi Huong Giang Tran Quoc Khai Dae-Young Im Young-Jae Ryoo |
| author_sort | Truong Thi Huong Giang |
| collection | DOAJ |
| description | Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance. |
| first_indexed | 2024-03-09T13:03:20Z |
| format | Article |
| id | doaj.art-00f016dbb15c477b8e66578d3a8c72b3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:03:20Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-00f016dbb15c477b8e66578d3a8c72b32023-11-30T21:50:40ZengMDPI AGSensors1424-82202022-07-012214514010.3390/s22145140Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D ImagesTruong Thi Huong Giang0Tran Quoc Khai1Dae-Young Im2Young-Jae Ryoo3Department of Electrical Engineering, Mokpo National University, Muan 58554, KoreaDepartment of Electrical Engineering, Mokpo National University, Muan 58554, KoreaComponents & Materials R&D Group, Korea Institute of Industrial Technology, Gwangju 61012, KoreaDepartment of Electrical and Control Engineering, Mokpo National University, Muan 58554, KoreaTomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.https://www.mdpi.com/1424-8220/22/14/5140tomato sucker detectiontomato pruningsemantic segmentation neural networkreal-timeRGB-D |
| spellingShingle | Truong Thi Huong Giang Tran Quoc Khai Dae-Young Im Young-Jae Ryoo Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images Sensors tomato sucker detection tomato pruning semantic segmentation neural network real-time RGB-D |
| title | Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images |
| title_full | Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images |
| title_fullStr | Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images |
| title_full_unstemmed | Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images |
| title_short | Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images |
| title_sort | fast detection of tomato sucker using semantic segmentation neural networks based on rgb d images |
| topic | tomato sucker detection tomato pruning semantic segmentation neural network real-time RGB-D |
| url | https://www.mdpi.com/1424-8220/22/14/5140 |
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