Advancing Image Segmentation Techniques for Strawberry Detection in Vision-Based Agricultural Robotics

Image segmentation is a fundamental component of vision-based agricultural robotics, enabling accurate fruit localization, disease detection, and automated harvesting. However, real-world strawberry fields present significant challenges due to irregular fruit morphology, dense foliage occlusions, variable ripeness, and strong illumination variability. Moreover, segmentation models trained on a single dataset often fail to generalize across domains, limiting their practical deployment. This paper presents a comprehensive benchmark of classical computer vision methods, convolutional neural networks, instance-based models, and transformer-based architectures across three heterogeneous public strawberry datasets: Db1 (instance segmentation), Db2 (lesion segmentation), and Db3 (semantic segmentation). A unified preprocessing and evaluation framework is adopted to ensure fair comparison using standard metrics, including Intersection-over-Union (IoU), Dice coefficient, Precision, and Recall. Extensive in-domain experiments demonstrate that deep learning models significantly outperform classical approaches, with U-Net and SegFormer achieving IoU values above 0.95 on Db1 and up to 0.83 on Db3. Cross-domain zero-shot evaluations reveal a substantial generalization gap, with U-Net suffering IoU drops of up to 100\%, while SegFormer consistently exhibits improved robustness and reduced cross-domain degradation across most transfer scenarios. To our knowledge, these results establish the first systematic multi-dataset benchmark for strawberry segmentation under domain shift, highlighting the importance of transformer-based architectures for robust agricultural perception and providing practical insights for real-world robotic deployment.