SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments
To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone netwo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/14/4457 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849733356914212864 |
|---|---|
| author | Xudong Lin Dehao Liao Zhiguo Du Bin Wen Zhihui Wu Xianzhi Tu |
| author_facet | Xudong Lin Dehao Liao Zhiguo Du Bin Wen Zhihui Wu Xianzhi Tu |
| author_sort | Xudong Lin |
| collection | DOAJ |
| description | To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone network, the LSKA module is embedded into the SPPF module to construct an SPPF-LSKA fusion module, enhancing multi-scale feature representation for peach targets. Second, an MPDIoU-based bounding box regression loss function replaces CIoU to improve localization accuracy for overlapping and occluded peaches. The DyHead Block is integrated into the detection head to form a DMDetect module, strengthening feature discrimination for small and occluded targets in complex backgrounds. To address insufficient feature fusion flexibility caused by scale variations from occlusion and illumination differences in multi-scale peach detection, a novel Adaptive Multi-Scale Fusion Pyramid (AMFP) module is proposed to enhance the neck network, improving flexibility in processing complex features. Experimental results demonstrate that SDA-YOLO achieves precision (P), recall (R), mAP@0.95, and mAP@0.5:0.95 of 90.8%, 85.4%, 90%, and 62.7%, respectively, surpassing YOLOv11n by 2.7%, 4.8%, 2.7%, and 7.2%. This verifies the method’s robustness in complex orchard environments and provides effective technical support for intelligent fruit harvesting and yield estimation. |
| format | Article |
| id | doaj-art-6c3243b0bf6946cbb4807eb983cabcac |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6c3243b0bf6946cbb4807eb983cabcac2025-08-20T03:08:02ZengMDPI AGSensors1424-82202025-07-012514445710.3390/s25144457SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard EnvironmentsXudong Lin0Dehao Liao1Zhiguo Du2Bin Wen3Zhihui Wu4Xianzhi Tu5College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510640, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510640, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510640, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510640, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510640, ChinaCollege of Arts, South China Agricultural University, Guangzhou 510640, ChinaTo address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone network, the LSKA module is embedded into the SPPF module to construct an SPPF-LSKA fusion module, enhancing multi-scale feature representation for peach targets. Second, an MPDIoU-based bounding box regression loss function replaces CIoU to improve localization accuracy for overlapping and occluded peaches. The DyHead Block is integrated into the detection head to form a DMDetect module, strengthening feature discrimination for small and occluded targets in complex backgrounds. To address insufficient feature fusion flexibility caused by scale variations from occlusion and illumination differences in multi-scale peach detection, a novel Adaptive Multi-Scale Fusion Pyramid (AMFP) module is proposed to enhance the neck network, improving flexibility in processing complex features. Experimental results demonstrate that SDA-YOLO achieves precision (P), recall (R), mAP@0.95, and mAP@0.5:0.95 of 90.8%, 85.4%, 90%, and 62.7%, respectively, surpassing YOLOv11n by 2.7%, 4.8%, 2.7%, and 7.2%. This verifies the method’s robustness in complex orchard environments and provides effective technical support for intelligent fruit harvesting and yield estimation.https://www.mdpi.com/1424-8220/25/14/4457YOLOpeachmulti-scale feature fusionobject detection |
| spellingShingle | Xudong Lin Dehao Liao Zhiguo Du Bin Wen Zhihui Wu Xianzhi Tu SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments Sensors YOLO peach multi-scale feature fusion object detection |
| title | SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments |
| title_full | SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments |
| title_fullStr | SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments |
| title_full_unstemmed | SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments |
| title_short | SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments |
| title_sort | sda yolo an object detection method for peach fruits in complex orchard environments |
| topic | YOLO peach multi-scale feature fusion object detection |
| url | https://www.mdpi.com/1424-8220/25/14/4457 |
| work_keys_str_mv | AT xudonglin sdayoloanobjectdetectionmethodforpeachfruitsincomplexorchardenvironments AT dehaoliao sdayoloanobjectdetectionmethodforpeachfruitsincomplexorchardenvironments AT zhiguodu sdayoloanobjectdetectionmethodforpeachfruitsincomplexorchardenvironments AT binwen sdayoloanobjectdetectionmethodforpeachfruitsincomplexorchardenvironments AT zhihuiwu sdayoloanobjectdetectionmethodforpeachfruitsincomplexorchardenvironments AT xianzhitu sdayoloanobjectdetectionmethodforpeachfruitsincomplexorchardenvironments |