Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS
To achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2664 |
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| author | Mengyuan Zhao Beibei Cui Yuehao Yu Xiaoyi Zhang Jiaxin Xu Fengzheng Shi Liang Zhao |
| author_facet | Mengyuan Zhao Beibei Cui Yuehao Yu Xiaoyi Zhang Jiaxin Xu Fengzheng Shi Liang Zhao |
| author_sort | Mengyuan Zhao |
| collection | DOAJ |
| description | To achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, as well as fruit occlusion. First, to enhance feature extraction at various levels of abstraction in the input data, this paper proposes a novel segment-wise convolution module, C2f-GB. This module performs convolution in stages on the feature map, generating more feature maps with fewer parameters and computational resources, thereby improving the model’s feature extraction capability while reducing parameter count and computational cost. Next, based on the YOLO v10 algorithm, this paper removes redundant detection layers to enhance the model’s ability to capture specific features and further reduce the number of parameters. This paper then integrates a bidirectional feature pyramid network (BiFPN) into the neck network to improve feature capture across different scales, enhancing the model’s ability to handle objects of varying sizes and complexities. Finally, we introduce a novel channel attention mechanism that allows the network to dynamically adjust its focus on channels, efficiently utilizing available information. Experimental results demonstrate that the improved YOLO-DGS model achieves a 2.6% increase in F1 score, 2.1% in recall, 2% in mAP50, and 1% in mAP50-95. Additionally, inference speed is improved by 12.5%, and the number of parameters is reduced by 26.3%. Compared to current mainstream lightweight object detection models, YOLO-DGS outperforms them, offering an efficient solution for the tomato harvesting robot vision system in natural environments. |
| format | Article |
| id | doaj-art-85f3c0db60f44af2a80d072bad3fbc47 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-85f3c0db60f44af2a80d072bad3fbc472025-08-20T03:53:01ZengMDPI AGSensors1424-82202025-04-01259266410.3390/s25092664Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGSMengyuan Zhao0Beibei Cui1Yuehao Yu2Xiaoyi Zhang3Jiaxin Xu4Fengzheng Shi5Liang Zhao6School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaTo achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, as well as fruit occlusion. First, to enhance feature extraction at various levels of abstraction in the input data, this paper proposes a novel segment-wise convolution module, C2f-GB. This module performs convolution in stages on the feature map, generating more feature maps with fewer parameters and computational resources, thereby improving the model’s feature extraction capability while reducing parameter count and computational cost. Next, based on the YOLO v10 algorithm, this paper removes redundant detection layers to enhance the model’s ability to capture specific features and further reduce the number of parameters. This paper then integrates a bidirectional feature pyramid network (BiFPN) into the neck network to improve feature capture across different scales, enhancing the model’s ability to handle objects of varying sizes and complexities. Finally, we introduce a novel channel attention mechanism that allows the network to dynamically adjust its focus on channels, efficiently utilizing available information. Experimental results demonstrate that the improved YOLO-DGS model achieves a 2.6% increase in F1 score, 2.1% in recall, 2% in mAP50, and 1% in mAP50-95. Additionally, inference speed is improved by 12.5%, and the number of parameters is reduced by 26.3%. Compared to current mainstream lightweight object detection models, YOLO-DGS outperforms them, offering an efficient solution for the tomato harvesting robot vision system in natural environments.https://www.mdpi.com/1424-8220/25/9/2664tomatoripeness detectionYOLO-DGSC2f-GBBiFPNchannel attention |
| spellingShingle | Mengyuan Zhao Beibei Cui Yuehao Yu Xiaoyi Zhang Jiaxin Xu Fengzheng Shi Liang Zhao Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS Sensors tomato ripeness detection YOLO-DGS C2f-GB BiFPN channel attention |
| title | Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS |
| title_full | Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS |
| title_fullStr | Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS |
| title_full_unstemmed | Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS |
| title_short | Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS |
| title_sort | intelligent detection of tomato ripening in natural environments using yolo dgs |
| topic | tomato ripeness detection YOLO-DGS C2f-GB BiFPN channel attention |
| url | https://www.mdpi.com/1424-8220/25/9/2664 |
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