DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism
With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineap...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1523552/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583738409615360 |
|---|---|
| author | Cong Lin Wencheng Jiang Weiye Zhao Lilan Zou Zhong Xue |
| author_facet | Cong Lin Wencheng Jiang Weiye Zhao Lilan Zou Zhong Xue |
| author_sort | Cong Lin |
| collection | DOAJ |
| description | With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineapple fields are captured by drones, the fruits are often obscured by the pineapple leaf crowns due to their appearance and planting characteristics. Additionally, the background in pineapple fields is relatively complex, and current mainstream target detection algorithms are known to perform poorly in detecting small targets under occlusion conditions in such complex backgrounds. To address these issues, an improved YOLOv8 target detection algorithm, named DPD-YOLO (Dense-Pineapple-Detection YOU Only Look Once), has been proposed for the detection of pineapples in complex environments. The DPD-YOLO model is based on YOLOv8 and introduces the attention mechanism (Coordinate Attention) to enhance the network’s ability to extract features of pineapples in complex backgrounds. Furthermore, the small target detection layer has been fused with BiFPN (Bi-directional Feature Pyramid Network) to strengthen the integration of multi-scale features and enrich the extraction of semantic features. At the same time, the original YOLOv8 detection head has been replaced by the RT-DETR detection head, which incorporates Cross-Attention and Self-Attention mechanisms that improve the model’s detection accuracy. Additionally, Focaler-IoU has been employed to improve CIoU, allowing the network to focus more on small targets. Finally, high-resolution images of the pineapple fields were captured using drones to create a dataset, and extensive experiments were conducted. The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The mAP@0.5 reached 62.0%, representing an improvement of 6.6% over the original YOLOv8 algorithm, Precision increased by 2.7%, Recall improved by 13%, and F1-score rose by 10.3%. |
| format | Article |
| id | doaj-art-b72d9d53f8bb47c989e9378ed21faf6f |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-b72d9d53f8bb47c989e9378ed21faf6f2025-01-28T06:41:16ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011610.3389/fpls.2025.15235521523552DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanismCong Lin0Wencheng Jiang1Weiye Zhao2Lilan Zou3Zhong Xue4School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaSouth Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, ChinaWith the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineapple fields are captured by drones, the fruits are often obscured by the pineapple leaf crowns due to their appearance and planting characteristics. Additionally, the background in pineapple fields is relatively complex, and current mainstream target detection algorithms are known to perform poorly in detecting small targets under occlusion conditions in such complex backgrounds. To address these issues, an improved YOLOv8 target detection algorithm, named DPD-YOLO (Dense-Pineapple-Detection YOU Only Look Once), has been proposed for the detection of pineapples in complex environments. The DPD-YOLO model is based on YOLOv8 and introduces the attention mechanism (Coordinate Attention) to enhance the network’s ability to extract features of pineapples in complex backgrounds. Furthermore, the small target detection layer has been fused with BiFPN (Bi-directional Feature Pyramid Network) to strengthen the integration of multi-scale features and enrich the extraction of semantic features. At the same time, the original YOLOv8 detection head has been replaced by the RT-DETR detection head, which incorporates Cross-Attention and Self-Attention mechanisms that improve the model’s detection accuracy. Additionally, Focaler-IoU has been employed to improve CIoU, allowing the network to focus more on small targets. Finally, high-resolution images of the pineapple fields were captured using drones to create a dataset, and extensive experiments were conducted. The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The mAP@0.5 reached 62.0%, representing an improvement of 6.6% over the original YOLOv8 algorithm, Precision increased by 2.7%, Recall improved by 13%, and F1-score rose by 10.3%.https://www.frontiersin.org/articles/10.3389/fpls.2025.1523552/fullpineapple detectionUAVBiFPNYOLOv8coordinate attention |
| spellingShingle | Cong Lin Wencheng Jiang Weiye Zhao Lilan Zou Zhong Xue DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism Frontiers in Plant Science pineapple detection UAV BiFPN YOLOv8 coordinate attention |
| title | DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism |
| title_full | DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism |
| title_fullStr | DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism |
| title_full_unstemmed | DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism |
| title_short | DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism |
| title_sort | dpd yolo dense pineapple fruit target detection algorithm in complex environments based on yolov8 combined with attention mechanism |
| topic | pineapple detection UAV BiFPN YOLOv8 coordinate attention |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1523552/full |
| work_keys_str_mv | AT conglin dpdyolodensepineapplefruittargetdetectionalgorithmincomplexenvironmentsbasedonyolov8combinedwithattentionmechanism AT wenchengjiang dpdyolodensepineapplefruittargetdetectionalgorithmincomplexenvironmentsbasedonyolov8combinedwithattentionmechanism AT weiyezhao dpdyolodensepineapplefruittargetdetectionalgorithmincomplexenvironmentsbasedonyolov8combinedwithattentionmechanism AT lilanzou dpdyolodensepineapplefruittargetdetectionalgorithmincomplexenvironmentsbasedonyolov8combinedwithattentionmechanism AT zhongxue dpdyolodensepineapplefruittargetdetectionalgorithmincomplexenvironmentsbasedonyolov8combinedwithattentionmechanism |