Article: Lightweight improvement algorithm for target detection of Pu'er tea harvesting robotic arm based on YOLOv8 Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.8 pp.1 - 18 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. Inderscience Publishers - linking academia, business and industry through research

Title: Lightweight improvement algorithm for target detection of Pu'er tea harvesting robotic arm based on YOLOv8

Authors: Jing Xu; Wei Li

Addresses: School of Machinery and Transportation, Southwest Forestry University, Panlong District, Kunming City, Yunnan Province, China ' School of Machinery and Transportation, Southwest Forestry University, Panlong District, Kunming City, Yunnan Province, China

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.

Keywords: the intelligent harvesting of Pu'er tea; target detection; YOLOv8; lightweight.

DOI: 10.1504/IJICT.2025.145720

International Journal of Information and Communication Technology, 2025 Vol.26 No.8, pp.1 - 18

Received: 10 Sep 2024
Accepted: 05 Feb 2025
Published online: 16 Apr 2025 *

Title: Lightweight improvement algorithm for target detection of Pu'er tea harvesting robotic arm based on YOLOv8

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