YOLOv8-CTCD: An improved YOLOv8 for cherry tomato cluster detection in robotic harvesting

Publication Type

Conference Proceeding Article

Publication Date

10-2025

Abstract

Cherry tomato harvesting is generally performed manually. Robotic harvesting is gaining increasing interest from both academia and industry. This paper proposes a cherry tomato cluster detection algorithm based on YOLOv8, named YOLOv8-CTCD. First, the YOLOv8 input channels are adjusted to enable 4-channel RGB-D images as input. Subsequently, a CARAFE-M module is designed to replace the upsampling method in YOLOv8n. It maintains a lightweight architecture while achieving a larger receptive field, allowing effective aggregation of contextual information. In addition, it assigns greater weight to more important features. Moreover, a C2f-MLCA module is introduced into YOLOv8, which integrates information from feature maps at different levels and enhances the network’s capability of feature extraction. It also integrates the SPPELAN module to strengthen its feature fusion capability. YOLOv8-CTCD has been evaluated using a private cherry tomato dataset obtained from a greenhouse farm. The experimental results show that it achieves an mAP@50 of 93.8% and an mAP@50:90 of 68%, which represents improvements of 2.1% and 3% over YOLOv8n, respectively.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, October 5-8

First Page

1099

Last Page

1104

Identifier

10.1109/SMC58881.2025.11342668

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/SMC58881.2025.11342668

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