Journal of Agricultural Machinery
with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Comparative Performance Assessment of a Transplanting Robot Equipped with Machine Vision under Different YOLO Algorithms

Articles in Press

Document Type : Research Article- En

Authors

S. Khodatars
V. Rasooli Sharabiani

Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

10.22067/jam.2026.99265.1496
Abstract
In this research, the performance of a greenhouse transplanting robot with a machine vision system based on the YOLO algorithms (YOLOv8s, YOLOv8n, YOLOv8m, YOLOv9c, YOLOv11s, YOLOv11m, and YOLOv11n) in handling pepper, lettuce, and tomato seedlings was evaluated in terms of detection accuracy, processing speed, and transplanting efficiency in a greenhouse environment. A robot was designed with a three-degree-of-freedom robotic arm, a dual-pin gripper, and a 1280×720 resolution camera, which was able to detect and transplant seedlings automatically. After comparing the different YOLO algorithms, the results showed that YOLOv8s had the best balance between accuracy (86.52%) and processing time (1’605.67 milliseconds), and the transplanting success rate was highest for tomato seedlings (92.38%) and lowest for pepper seedlings (81.11%). Statistical analysis confirmed that significant differences existed between the species. This system demonstrated a strong potential for precision agriculture by reducing labour costs and enhancing productivity. With future optimisations, it could deliver improved performance across diverse environmental conditions.

Keywords

Greenhouse
Machine vision
Robotic Transplanting
YOLO algorithm
Subjects

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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Journal of Agricultural Machinery

Articles in Press, Accepted Manuscript
Available Online from 03 October 2026

  • Receive Date 09 June 2026
  • Revise Date 06 September 2026
  • Accept Date 14 September 2026
  • First Publish Date 03 October 2026

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