Original Articles Open access

YOLO-Banana-Seg: a lightweight and efficient model for rapid segmentation of banana bunches and stalks in complex orchards

Authors

Published
16 September 2026
Issue
Early Access
Section
Original Articles
DOI
10.4081/jae.2026.2095

Abstract

In banana orchards, accurate segmentation of bunches and stalks is vital for automated harvesting and yield estimation. However, color similarity between green bananas and leaves, irregular shapes, and occlusion makes instance segmentation highly challenging. This study proposes an improved YOLOv11-based segmentation model, YOLO-Banana-Seg, for banana bunches and stalks. We compiled a multi-condition banana dataset encompassing varying illumination intensities and occlusion levels, while the proposed model integrates multi-scale feature fusion and adaptive attention mechanisms to significantly improve occluded target segmentation accuracy. The results show that YOLO-Banana-Seg achieves high segmentation accuracy while reducing parameters and computational complexity, ensuring its applicability in resource-limited scenarios. Experiments demonstrate 97.4% accuracy and 82.9% recall with 16.7% fewer parameters than YOLOv11n, balancing precision and efficiency. The model's high precision and efficiency directly could support the development of harvesting robots and yield estimation systems for agricultural applications.

Keywords

Supporting Agencies

Research on Key Technologies of Laser Processing Control System, Ph.D. Research Start-Up Fund of Wuyi University, National Natural Science Foundation of China Project, Wuyi University, Hong Kong and Macau Joint Funding Scheme

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

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