Lightweight improvement algorithm for target detection of Pu'er tea harvesting robotic arm based on YOLOv8
Year: 2025
Type: article
Abstract: To tackle the challenges of recognition difficulties and constrained computational resources in Pu'er tea intelligent harvesting, this research develops an optimised, resource-efficient object detection algorithm built upon the YOLOv8n architecture for detecting tender shoots of Pu'er tea. The methodology incorporates three primary enhancements: first, replacing the standard Conv module with the Adown down-sampling component enhances detection precision, significantly boosts processing speed, and minimises model complexity; second, modifying the detection head to the LADH configuration cuts down parameter volume, further streamlining the model; third, integrating the AFGC attention mechanism refines detection accuracy. Experimental outcomes reveal that the optimised model achieves a 0.7% increase in mean average precision (mAP), accelerates detection speed by 482.9 FPS, and reduces model size by 1.9 MB compared to the baseline YOLOv8n. This work provides a technical foundation for advancing intelligent harvesting systems tailored for Pu'er tea cultivation.
Authors: Jing Xu, Wei Li
Institution: Southwest Forestry University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4409526078