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YOLO-SAM AgriScan: A Unified Framework for Ripe Strawberry Detection and Segmentation with Few-Shot and Zero-Shot Learning.

Sensors (Basel, Switzerland) · 18 Dec 2025 · 10.3390/s25247678

Abstract

Traditional segmentation methods are slow and rely on manual annotations, which are labor-intensive. To address these limitations, we propose YOLO-SAM AgriScan, a unified framework that combines the fast object detection capabilities of YOLOv11 with the zero-shot segmentation power of the Segment Anything Model 2 (SAM2). Our approach adopts a hybrid paradigm for on-plant ripe strawberry segmentation, wherein YOLOv11 is fine-tuned using a few-shot learning strategy with minimal annotated samples, and SAM2 performs mask generation without additional supervision. This architecture eliminates the bottleneck of pixel-wise manual annotation and enables the scalable and efficient segmentation of strawberries in both controlled and natural farm environments. Experimental evaluations on two datasets, a custom-collected dataset and a publicly available benchmark, demonstrate strong detection and segmentation performance in both full-data and data-constrained scenarios. The proposed framework achieved a mean Dice score of 0.95 and an IoU of 0.93 on our collected dataset and maintained competitive performance on public data (Dice: 0.95, IoU: 0.92), demonstrating its robustness, generalizability, and practical relevance in real-world agricultural settings. Our results highlight the potential of combining few-shot detection and zero-shot segmentation to accelerate the development of annotation-light, intelligent phenotyping systems.

Plant phenotyping relevance

イチゴ果実の検出・セグメンテーションを行う画像ベース手法を開発し、複数データセットで性能評価している。単なる収穫対象の位置特定ではなく、果実マスク抽出を行うため、植物表現型取得手法が中心である。

abstractOur approach adopts a hybrid paradigm for on-plant ripe strawberry segmentation
abstractExperimental evaluations on two datasets, a custom-collected dataset and a publicly available benchmark, demonstrate strong detection and segmentation performance
abstractaccelerate the development of annotation-light, intelligent phenotyping systems.

Code and data availability

The paper uses a public Roboflow Universe strawberry segmentation dataset (D2) directly for its detection/segmentation experiments, which qualifies as a paper-specific public asset. The authors' custom greenhouse dataset (D1) is only available upon request, and no authors' analysis code is deposited (Ultralytics is a C

Datasetpublic

3007-022-00866-2. 22. Song Y., Wang T., Cai P., Mondal S.K., Sahoo J.P. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. ACM Comput. Surv. 2023;55:1–40. doi: 10.1145/3582688. 23. Objectdetection Strawberry Seg Dataset. 2024. [(accessed on 10 September 2025)]. Available online: https://universe.roboflow.com/objectdetection-mnlwg/strawberry_seg-zkh1y . 24. Sekachev B., Manovich N., Zhiltsov M., Zhavoronkov A., Kalinin D., Hoff B., TOsmanov, Kruchinin D., Zankevich A., DmitriySidnev, et al. opencv/cvat: V1.1.0. 2020. [(accessed on 13 April 2025)]. Available online: https://zenodo.org/records/4009388 . 25. Ronneberger O., Fischer P., Brox T. U-

Open resource ↗strawberry_seg-zkh1y · lines:316-338

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