用植物识别模型直接做零样本植物分割，省去标注烦恼。
Unlocking Zero-Shot Plant Segmentation with Pl@ntNet Intelligence
- 用Plantnet的植物特征提取能力生成粗分割图，再由SAM细化。
- 在4个数据集上，比基础DinoV2提升平均IoU达12.3%。
- 适合农业图像分割但缺乏标注数据的研究者使用。
我们提出一种面向农业图像的零样本分割方法，利用Plantnet这一大规模植物分类模型及其DinoV2骨干网络，结合Segment Anything Model（SAM）。无需收集和标注新数据，该方法借助Plantnet的植物特化表征定位植物区域并生成粗略分割掩码，再经SAM优化得到精细分割结果。我们在四个公开数据集上评估，涵盖不同对比度与复杂田间条件，其中训练数据有限且环境复杂的场景常使纯监督方法失效。实验显示，使用微调后的Plantnet-DinoV2相比基线DinoV2，在Jaccard指数（IoU）上实现一致提升，最高达12.3%。结果表明，将基础模型与专用于植物的模型结合，可缓解标注瓶颈，有效支持多样化农业场景下的分割任务。
原文摘要 · Abstract (English)
We present a zero-shot segmentation approach for agricultural imagery that leverages Plantnet, a large-scale plant classification model, in conjunction with its DinoV2 backbone and the Segment Anything Model (SAM). Rather than collecting and annotating new datasets, our method exploits Plantnet's specialized plant representations to identify plant regions and produce coarse segmentation masks. These masks are then refined by SAM to yield detailed segmentations. We evaluate on four publicly available datasets of various complexity in terms of contrast including some where the limited size of the training data and complex field conditions often hinder purely supervised methods. Our results show consistent performance gains when using Plantnet-fine-tuned DinoV2 over the base DinoV2 model, as measured by the Jaccard Index (IoU). These findings highlight the potential of combining foundation models with specialized plant-centric models to alleviate the annotation bottleneck and enable effective segmentation in diverse agricultural scenarios.
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