Comparative Performance Analysis of YOLOv5 and YOLOv8 for Tomato Detection in Agricultural Robotics

Date

2026-01-01

Author

Kıvrak, Eslem
Simav, Orhun Erke
Şahin, Arda
Alraee, Abdullah
Albaroudi, Mohammad
Alahmad, Raji
ALRAIE, HUSSAM
NESİMOĞLU, TAYFUN

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Automated tomato detection is essential for robotic harvesting and reducing labor dependency in agriculture. However, robust tomato detection under real farming conditions remains a critical challenge in computer vision. This study aimsto develop a reliable system that can accurately recognize tomatoes in images and clearly annotate their locations. Farmers rely on automation to help automate their harvesting operations and reduce the time spent on manual labor. Tomatoes present a difficult test case because they vary widely in color, size, shape, and ripeness. Therefore, selected tomatoes were harvested specifically to evaluate whether current AI models can handle these real-world variations. YOLOv5 and YOLOv8 were compared to evaluate their performance across various real-time object detection algorithms. Overall, the tomato detection framework demonstrates that machine learning can significantly enhance agricultural efficiency and support more informed farm management.

Subject Keywords

Agricultural Harvest Technologies, Object Detection, Tomato Harvesting, YOLOv5, YOLOv8

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105030191786&origin=inward
https://hdl.handle.net/11511/118547

Collections

Department of Civil Engineering, Conference / Seminar