Enhanced Image Annotation in Wild Blueberry ( Vaccinium angustifolium Ait.) Fields Using Sequential Zero-Shot Detection and Segmentation Models.

Abstract

This research addresses the critical need for efficient image annotation in precision agriculture, using the wild blueberry ( Vaccinium angustifolium Ait.) cropping system as a representative application to enable data-driven crop management. Tasks such as automated berry ripeness detection, plant disease identification, plant growth stage monitoring, and weed detection rely on extensive annotated datasets. However, manual annotation is labor-intensive, time-consuming, and impractical for large-scale agricultural systems. To address this challenge, this study evaluates an automated annotation pipeline that integrates zero-shot detection models from two frameworks (Grounding DINO and YOLO-World) with the Segment Anything Model version 2 (SAM2). The models were tested on detecting and segmenting ripe wild blueberries, developmental wild blueberry buds, hair fescue ( Festuca filiformis Pourr.), and red leaf disease ( Exobasidium vaccinii ). Grounding DINO consistently outperformed YOLO-World, with its Swin-T achieving mean Intersection over Union (mIoU) scores of 0.694 ± 0.175 for fescue grass and 0.905 ± 0.114 for red leaf disease when paired with SAM2-Large. For ripe wild blueberry detection, Swin-B with SAM2-Small achieved the highest performance (mIoU of 0.738 ± 0.189). Whereas for wild blueberry buds, Swin-B with SAM2-Large yielded the highest performance (0.751 ± 0.154). Processing times were also evaluated, with SAM2-Tiny, Small, and Base demonstrating the shortest durations when paired with Swin-T (0.30-0.33 s) and Swin-B (0.35-0.38 s). SAM2-Large, despite higher segmentation accuracy, had significantly longer processing times (significance level α = 0.05), making it less practical for real-time applications. This research offers a scalable solution for rapid, accurate annotation of agricultural images, improving targeted crop management. Future research should optimize these models for different cropping systems, such as orchard-based agriculture, row crops, and greenhouse farming, and expand their application to diverse crops to validate their generalizability.

Plant phenotyping relevance

植物の果実・芽・病徴を対象に、ゼロショット検出とSAM2による検出・セグメンテーション注釈パイプラインを開発・評価しており、植物表現型の画像取得・抽出手法が研究の中心である。

Code and data availability

The supplied blocks describe a wild blueberry image dataset (buds, berries, red leaf, hair fescue) and a zero-shot detection/segmentation pipeline, but contain no public deposit, availability statement, or authors' URL for the dataset, images, annotations, or analysis code. The only URL present (Perennia crop schedule)

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