Research on a UAV-Based Litchi Flower Cluster Detection Method Using an Improved YOLO11n

Baoxia Sun, Yanggang Ou, Jiatong Tang, Shuqin Cai, Yutao Chen, Wenyi Bao, Juntao Xiong () and Yanan Li
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Baoxia Sun: College of Electrical Technology, Guangdong Mechanical and Electrical Polytechnic, Guangzhou 510515, China
Yanggang Ou: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Jiatong Tang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Shuqin Cai: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yutao Chen: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Wenyi Bao: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Juntao Xiong: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yanan Li: School of Engineering and Informatics, University of Sussex, Brighton BN1 9RH, UK

Agriculture, 2025, vol. 15, issue 18, 1-25

Abstract: The number of litchi flower clusters is an important indicator for predicting the fruit set rate and yield of litchi trees. However, their dense distribution, scale variation, and occlusion make it very challenging to achieve high-precision intelligent detection of litchi flower clusters in natural scenes. This study proposes a UAV-based litchi flower cluster detection method using an improved YOLO11n. First, the backbone introduces a WTConv-improved C3k2 module (C3k2_WTConv) to enhance feature extraction capability; then, the neck adopts a SlimNeck structure for efficient multi-scale fusion and parameter reduction; and finally, the DySample module replaces the original up-sampling to mitigate accuracy loss caused by scale variation. Experimental results on UAV-based litchi flower cluster detection show that the model achieves an mAP@0.5 of 87.28%, with recall, precision, F1-score , and mAP@0.5 improved by 6.26%, 4.03%, 5.14%, and 5.16% over YOLO11n. Computational cost and parameters decrease by 7.69% and 2.37%, respectively. In counting tasks, MAE , RMSE , MAPE , and R 2 reach 5.23, 6.89, 9.72%, and 0.9205, indicating excellent performance. The proposed method offers efficient and accurate technical support for intelligent litchi blossom management and yield estimation, and provides optimization strategies applicable to dense multi-scale object detection tasks.

Keywords: litchi flower detection; UAV; YOLO11; multi-scale 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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