An Efficient Detector for Automatic Tomato Classification Systems
Nowadays, artificial intelligence and robotics have been deployed in almost all areas of human life. Especially in agriculture, it has helped people free up labor, speed up production, and ensure product quality. This research aims to develop a vision-based tomato detector to support robots and auto...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10815732/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832586860615958528 |
|---|---|
| author | Duy-Linh Nguyen Xuan-Thuy Vo Adri Priadana Jehwan Choi Kang-Hyun Jo |
| author_facet | Duy-Linh Nguyen Xuan-Thuy Vo Adri Priadana Jehwan Choi Kang-Hyun Jo |
| author_sort | Duy-Linh Nguyen |
| collection | DOAJ |
| description | Nowadays, artificial intelligence and robotics have been deployed in almost all areas of human life. Especially in agriculture, it has helped people free up labor, speed up production, and ensure product quality. This research aims to develop a vision-based tomato detector to support robots and automatic tomato classification systems. In this idea, the original convolution blocks in the Backbone and Neck modules of the YOLOv8n architecture are replaced by a new version, called Receptive Field Attention Convolution (RFAConv). The model was trained and evaluated using four benchmarks, including the Laboro Tomato, Tomato Plantfactory, Sai Gon University, and CubeAI datasets. It achieved the best performance at 89.8% of mAP@0.5 and 65.2% of mAP@0.5:0.95 on the Tomato Plantfactory dataset. These results show that the proposed network is superior to others under a fixed set of experimental conditions. The video demonstration, datasets, weights, and models are available at <uri>https://bit.ly/3XdLrLM</uri>. |
| format | Article |
| id | doaj-art-632a233fc22e48d3a5a3071cabe62631 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-632a233fc22e48d3a5a3071cabe626312025-01-25T00:01:06ZengIEEEIEEE Access2169-35362025-01-0113140731408210.1109/ACCESS.2024.352234910815732An Efficient Detector for Automatic Tomato Classification SystemsDuy-Linh Nguyen0https://orcid.org/0000-0001-6184-4133Xuan-Thuy Vo1https://orcid.org/0000-0002-7411-0697Adri Priadana2https://orcid.org/0000-0002-1553-7631Jehwan Choi3https://orcid.org/0009-0005-8494-2170Kang-Hyun Jo4https://orcid.org/0000-0002-4937-7082Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaNowadays, artificial intelligence and robotics have been deployed in almost all areas of human life. Especially in agriculture, it has helped people free up labor, speed up production, and ensure product quality. This research aims to develop a vision-based tomato detector to support robots and automatic tomato classification systems. In this idea, the original convolution blocks in the Backbone and Neck modules of the YOLOv8n architecture are replaced by a new version, called Receptive Field Attention Convolution (RFAConv). The model was trained and evaluated using four benchmarks, including the Laboro Tomato, Tomato Plantfactory, Sai Gon University, and CubeAI datasets. It achieved the best performance at 89.8% of mAP@0.5 and 65.2% of mAP@0.5:0.95 on the Tomato Plantfactory dataset. These results show that the proposed network is superior to others under a fixed set of experimental conditions. The video demonstration, datasets, weights, and models are available at <uri>https://bit.ly/3XdLrLM</uri>.https://ieeexplore.ieee.org/document/10815732/Convolutional neural network (CNN)tomato detection and classificationreceptive field attention convolution (RFAConv)YOLOv8n |
| spellingShingle | Duy-Linh Nguyen Xuan-Thuy Vo Adri Priadana Jehwan Choi Kang-Hyun Jo An Efficient Detector for Automatic Tomato Classification Systems IEEE Access Convolutional neural network (CNN) tomato detection and classification receptive field attention convolution (RFAConv) YOLOv8n |
| title | An Efficient Detector for Automatic Tomato Classification Systems |
| title_full | An Efficient Detector for Automatic Tomato Classification Systems |
| title_fullStr | An Efficient Detector for Automatic Tomato Classification Systems |
| title_full_unstemmed | An Efficient Detector for Automatic Tomato Classification Systems |
| title_short | An Efficient Detector for Automatic Tomato Classification Systems |
| title_sort | efficient detector for automatic tomato classification systems |
| topic | Convolutional neural network (CNN) tomato detection and classification receptive field attention convolution (RFAConv) YOLOv8n |
| url | https://ieeexplore.ieee.org/document/10815732/ |
| work_keys_str_mv | AT duylinhnguyen anefficientdetectorforautomatictomatoclassificationsystems AT xuanthuyvo anefficientdetectorforautomatictomatoclassificationsystems AT adripriadana anefficientdetectorforautomatictomatoclassificationsystems AT jehwanchoi anefficientdetectorforautomatictomatoclassificationsystems AT kanghyunjo anefficientdetectorforautomatictomatoclassificationsystems AT duylinhnguyen efficientdetectorforautomatictomatoclassificationsystems AT xuanthuyvo efficientdetectorforautomatictomatoclassificationsystems AT adripriadana efficientdetectorforautomatictomatoclassificationsystems AT jehwanchoi efficientdetectorforautomatictomatoclassificationsystems AT kanghyunjo efficientdetectorforautomatictomatoclassificationsystems |