YOLOv8n-RMB: UAV Imagery Rubber Milk Bowl Detection Model for Autonomous Robots’ Natural Latex Harvest

Yunfan Wang, Lin Yang (), Pengze Zhong, Xin Yang, Chuanchuan Su, Yi Zhang and Aamir Hussain
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Yunfan Wang: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Lin Yang: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Pengze Zhong: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Xin Yang: Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany
Chuanchuan Su: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Yi Zhang: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Aamir Hussain: Institute of Computing, MNS University of Agriculture, Multan 60000, Pakistan

Agriculture, 2025, vol. 15, issue 19, 1-23

Abstract: Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations.

Keywords: natural rubber milk; tapping robot; UAV imagery; YOLO; object detection (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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