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Article

RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images

1
School of Information, Yunnan Normal University, Kunming 650500, China
2
Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, Kunming 650500, China
3
Centre for Planning and Policy Research, Yunnan Institute of Forest Inventory and Planning, Kunming 650051, China
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(4), 419; https://doi.org/10.3390/horticulturae11040419
Submission received: 24 February 2025 / Revised: 23 March 2025 / Accepted: 11 April 2025 / Published: 14 April 2025
(This article belongs to the Section Postharvest Biology, Quality, Safety, and Technology)

Abstract

Accurate walnut yield prediction is crucial for the development of the walnut industry. Traditional manual counting methods are limited by labor and time costs, leading to inaccurate walnut quantity assessments. In this paper, we propose a walnut detection method based on UAV (UAV means Unmanned Aerial Vehicle) remote sensing imagery to improve the walnut yield prediction accuracy. Based on the YOLOv11 network, we propose several improvements to enhance the multi-scale object detection capability while achieving a more lightweight model structure. Specifically, we reconstruct the feature fusion network with a hierarchical scale-based feature pyramid structure and implement lightweight improvements to the feature extraction component. These modifications result in the RSWD-YOLO network (RSWD means remote sensing walnut detection; YOLO means ‘You Only Look Once’, and it is the specific abbreviation used for a series of object detection algorithms), which is specifically designed for walnut detection. Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. Through model deployment and testing on small edge devices, we demonstrate the feasibility of our proposed method. The detection algorithm achieves 86.1% mean Average Precision on the walnut dataset while maintaining operational functionality on small edge devices. The experimental results demonstrate that our proposed UAV remote sensing-based walnut detection method has a significant practical application value and can provide valuable insights for future research in related fields.
Keywords: remote sensing images; walnut; object detection; YOLOv11; knowledge distillation remote sensing images; walnut; object detection; YOLOv11; knowledge distillation

Share and Cite

MDPI and ACS Style

Wang, Y.; Yang, X.; Wang, H.; Wang, H.; Chen, Z.; Yun, L. RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images. Horticulturae 2025, 11, 419. https://doi.org/10.3390/horticulturae11040419

AMA Style

Wang Y, Yang X, Wang H, Wang H, Chen Z, Yun L. RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images. Horticulturae. 2025; 11(4):419. https://doi.org/10.3390/horticulturae11040419

Chicago/Turabian Style

Wang, Yansong, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, and Lijun Yun. 2025. "RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images" Horticulturae 11, no. 4: 419. https://doi.org/10.3390/horticulturae11040419

APA Style

Wang, Y., Yang, X., Wang, H., Wang, H., Chen, Z., & Yun, L. (2025). RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images. Horticulturae, 11(4), 419. https://doi.org/10.3390/horticulturae11040419

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