Squeeze Every Bit of Insight: Leveraging Few-shot Models with a Compact Support Set for Domain Transfer in Object Detection from Pineapple Fields

Object detection (OD) typically demands large annotated datasets and substantial computational resources. To address these challenges, we propose a novel two-stage pipeline that integrates Visual Foundation Models (VFMs) for object proposal generation with few-shot learning models enhanced by Mahalanobis distance-based classification. Our approach improves upon traditional Euclidean-based methods by incorporating data covariance through support and context prototypes. We specifically focus on scenarios with only just a few annotated images, reflecting real-world limitations where large-scale labeling is not feasible. Validated on pineapple detection from drone imagery, our method outperforms state-of-The-Art (SOTA) few-shot models using minimal labeled data. Extensive experiments show that FastSAM, when combined with a Mahalanobis distance variant that applies singular value decomposition (SVD) and diagonal loading for regularization, achieves the highest mean average precision (mAP), offering a practical and effective tool for crop monitoring and management.