RFA-YOLOv8: A Robust Tea Bud Detection Model with Adaptive Illumination Enhancement for Complex Orchard Environments

Qiuyue Yang, Jinan Gu (), Tao Xiong, Qihang Wang, Juan Huang, Yidan Xi and Zhongkai Shen
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Qiuyue Yang: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Jinan Gu: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Tao Xiong: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Qihang Wang: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Juan Huang: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Yidan Xi: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Zhongkai Shen: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 18, 1-23

Abstract: Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe overlap, and the similarity in color between tea shoots and the background. Consequently, this paper proposes an improved target detection algorithm, RFA-YOLOv8, based on YOLOv8, which aims to enhance the detection accuracy and robustness of tea shoots in natural environments. First, a self-constructed dataset containing images of tea shoots under various lighting conditions is created for model training and evaluation. Second, the multi-scale feature extraction capability of the model is enhanced by introducing RFCAConv along with the optimized SPPFCSPC module, while the spatial perception ability is improved by integrating the RFAConv module. Finally, the EIoU loss function is employed instead of CIoU to optimize the accuracy of the bounding box positioning. The experimental results demonstrate that the improved model achieves 84.1% and 58.7% in mAP@0.5 and mAP@0.5:0.95, respectively, which represent increases of 3.6% and 5.5% over the original YOLOv8. Robustness is evaluated under strong, moderate, and dim lighting conditions, yielding improvements of 6.3% and 7.1%. In dim lighting, mAP@0.5 and mAP@0.5:0.95 improve by 6.3% and 7.1%, respectively. The findings of this research provide an effective solution for the high-precision detection of tea shoots in complex lighting environments and offer theoretical and technical support for the development of smart tea gardens and automated picking.

Keywords: tea shoot recognition; YOLOv8; small target detection; natural environment (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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