Tomato ripeness and stem recognition based on improved YOLOX
Abstract To address the challenges of unbalanced class labels with varying maturity levels of tomato fruits and low recognition accuracy for both fruits and stems in intelligent harvesting, we propose the YOLOX-SE-GIoU model for identifying tomato fruit maturity and stems. The SE focus module was in...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84869-0 |
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| author | Yanwen Li Juxia Li Lei Luo Lingqi Wang Qingyu Zhi |
| author_facet | Yanwen Li Juxia Li Lei Luo Lingqi Wang Qingyu Zhi |
| author_sort | Yanwen Li |
| collection | DOAJ |
| description | Abstract To address the challenges of unbalanced class labels with varying maturity levels of tomato fruits and low recognition accuracy for both fruits and stems in intelligent harvesting, we propose the YOLOX-SE-GIoU model for identifying tomato fruit maturity and stems. The SE focus module was incorporated into YOLOX to improve the identification accuracy, addressing the imbalance in the number of tomato fruits and stems. Additionally, we optimized the loss function to GIoU loss to minimize discrepancies across different scales of fruits and stems. The mean average precision (mAP) of the improved YOLOX-SE-GIoU model reaches 92.17%. Compared to YOLOv4, YOLOv5, YOLOv7, and YOLOX models, the improved model shows an improvement of 1.17–22.21%. The average precision (AP) for unbalanced semi-ripe tomatoes increased by 1.68–26.66%, while the AP for stems increased by 3.78–45.03%. Experimental results demonstrate that the YOLOX-SE-GIoU model exhibits superior overall recognition performance for unbalanced and scale-variant samples compared to the original model and other models in the same series. It effectively reduces false and missed detections during tomato harvesting, improving the identification accuracy of tomato fruits and stems. The findings of this work provide a technical foundation for developing advanced fruit harvesting techniques. |
| format | Article |
| id | doaj-art-bc0dd391cfba4b7f9733748e5daea64c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bc0dd391cfba4b7f9733748e5daea64c2025-01-19T12:21:15ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-84869-0Tomato ripeness and stem recognition based on improved YOLOXYanwen Li0Juxia Li1Lei Luo2Lingqi Wang3Qingyu Zhi4College of Information Science and Engineering, Shanxi Agricultural UniversityCollege of Information Science and Engineering, Shanxi Agricultural UniversityCollege of Information Science and Engineering, Shanxi Agricultural UniversityCollege of Information Science and Engineering, Shanxi Agricultural UniversityCollege of Information Science and Engineering, Shanxi Agricultural UniversityAbstract To address the challenges of unbalanced class labels with varying maturity levels of tomato fruits and low recognition accuracy for both fruits and stems in intelligent harvesting, we propose the YOLOX-SE-GIoU model for identifying tomato fruit maturity and stems. The SE focus module was incorporated into YOLOX to improve the identification accuracy, addressing the imbalance in the number of tomato fruits and stems. Additionally, we optimized the loss function to GIoU loss to minimize discrepancies across different scales of fruits and stems. The mean average precision (mAP) of the improved YOLOX-SE-GIoU model reaches 92.17%. Compared to YOLOv4, YOLOv5, YOLOv7, and YOLOX models, the improved model shows an improvement of 1.17–22.21%. The average precision (AP) for unbalanced semi-ripe tomatoes increased by 1.68–26.66%, while the AP for stems increased by 3.78–45.03%. Experimental results demonstrate that the YOLOX-SE-GIoU model exhibits superior overall recognition performance for unbalanced and scale-variant samples compared to the original model and other models in the same series. It effectively reduces false and missed detections during tomato harvesting, improving the identification accuracy of tomato fruits and stems. The findings of this work provide a technical foundation for developing advanced fruit harvesting techniques.https://doi.org/10.1038/s41598-024-84869-0Recognition of tomato maturityDeep learningFruit stem recognitionAttention moduleLoss function |
| spellingShingle | Yanwen Li Juxia Li Lei Luo Lingqi Wang Qingyu Zhi Tomato ripeness and stem recognition based on improved YOLOX Scientific Reports Recognition of tomato maturity Deep learning Fruit stem recognition Attention module Loss function |
| title | Tomato ripeness and stem recognition based on improved YOLOX |
| title_full | Tomato ripeness and stem recognition based on improved YOLOX |
| title_fullStr | Tomato ripeness and stem recognition based on improved YOLOX |
| title_full_unstemmed | Tomato ripeness and stem recognition based on improved YOLOX |
| title_short | Tomato ripeness and stem recognition based on improved YOLOX |
| title_sort | tomato ripeness and stem recognition based on improved yolox |
| topic | Recognition of tomato maturity Deep learning Fruit stem recognition Attention module Loss function |
| url | https://doi.org/10.1038/s41598-024-84869-0 |
| work_keys_str_mv | AT yanwenli tomatoripenessandstemrecognitionbasedonimprovedyolox AT juxiali tomatoripenessandstemrecognitionbasedonimprovedyolox AT leiluo tomatoripenessandstemrecognitionbasedonimprovedyolox AT lingqiwang tomatoripenessandstemrecognitionbasedonimprovedyolox AT qingyuzhi tomatoripenessandstemrecognitionbasedonimprovedyolox |