A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots

Fei Yuan, Jinpeng Wang (), Wenqin Ding (), Song Mei, Chenzhe Fang, Sunan Chen and Hongping Zhou
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Fei Yuan: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Jinpeng Wang: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Wenqin Ding: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Song Mei: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Chenzhe Fang: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Sunan Chen: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Hongping Zhou: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China

Agriculture, 2025, vol. 15, issue 11, 1-21

Abstract: Dragon fruit detection in natural environments remains challenged by limited accuracy and deployment difficulties, primarily due to variable lighting and occlusions from branches. To enhance detection accuracy and satisfy the deployment constraints of edge devices, we propose YOLOv10n-CGD, a lightweight and efficient dragon fruit detection method designed for robotic harvesting applications. The method builds upon YOLOv10 and integrates Gated Convolution (gConv) into the C2f module, forming a novel C2f-gConv structure that effectively reduces model parameters and computational complexity. In addition, a Global Attention Mechanism (GAM) is inserted between the backbone and the feature fusion layers to enrich semantic representations and improve the detection of occluded fruits. Furthermore, the neck network integrates a Dynamic Sample (DySample) operator to enhance the spatial restoration of high-level semantic features. The experimental results demonstrate that YOLOv10n-CGD significantly improves performance while reducing model size from 5.8 MB to 4.5 MB—a 22.4% decrease. The mAP improves from 95.1% to 98.1%, with precision and recall reaching 97.1% and 95.7%, respectively. The observed improvements are statistically significant ( p < 0.05). Moreover, detection speeds of 44.9 FPS and 17.2 FPS are achieved on Jetson AGX Orin and Jetson Nano, respectively, demonstrating strong real-time capabilities and suitability for deployment. In summary, YOLOv10n-CGD enables high-precision, real-time dragon fruit detection while preserving model compactness, offering robust technical support for future robotic harvesting systems and smart agricultural terminals.

Keywords: dragon fruit; object detection; YOLO; lightweight; mobile deployment (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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