Leveraging Zero-Shot Detection Mechanisms to Accelerate Image Annotation for Machine Learning in Wild Blueberry (Vaccinium angustifolium Ait.)

This study conducted an analysis of zero-shot detection capabilities using two frameworks, YOLO-World and Grounding DINO, on a selection of images in the wild blueberry (Vaccinium angustifolium Ait.) cropping system. The datasets included ripe wild blueberries, hair fescue (Festuca filiformis Pourr.), blueberry buds, and red leaf disease (Exobasidium vaccinii). Key performance metrics such as Intersection over Union (IoU), precision, recall, and F1 score were utilized for model comparison. Grounding DINO consistently achieved superior performance across all metrics and datasets, achieving significantly higher mean IoUs on berries, red leaf, hair fescue, and buds (0.642, 0.921, 0.735, and 0.629, respectively) compared to YOLO-World (0.516, 0.567, 0.232, and 0.408, respectively). Evidenced by their high recall rates relative to precision, the models displayed a preference for identifying true positives at the cost of increasing false positives. Grounding DINO's higher precision (overall mean of 0.672), despite the tendency to over-detect, indicated a better balance in minimizing false positives than YOLO-World (overall mean of 0.501). These findings contrast with the foundational study of YOLO-World where it demonstrated superior performance on standard datasets, highlighting the importance of dataset characteristics and optimization processes in model performance. The practical implications of this study include providing a solution for accelerated object detection image annotation in the wild blueberry cropping system. This work, representing a significant advancement in facilitating accurate and efficient annotation of wild blueberry datasets, guides future research in the application of zero-shot detection models to agricultural datasets.