PlantSeg：一个用于植物病害分割的大规模野外数据集
PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
AI总结：
针对现有植物病害数据集缺少分割标注且多限于实验室场景的问题，本文构建大规模野外 PlantSeg 数据集，含11,400张病害分割图像和8,000张健康图像，为病害分割算法提供高质量基准。
摘要：
植物病害对农业构成重大威胁。为了保障作物产量，需要进行正确诊断和有效治疗。为了使诊断过程自动化，图像分割通常被用于精确识别患病区域，从而推动精准农业发展。开发鲁棒的植物病害图像分割模型需要在大量图像上具备高质量标注。然而，现有植物病害数据集通常缺少分割标签，并且往往局限于受控实验室环境，无法充分反映自然环境的复杂性。受这一事实启发，我们建立了 PlantSeg，这是一个用于植物病害的大规模分割数据集。PlantSeg 在三个关键方面区别于现有数据集。（1）标注类型：不同于大多数仅包含类别标签或边界框的现有数据集，PlantSeg 中的每张图像都包含详细且高质量的分割掩码，并关联植物类型和病害名称。（2）图像来源：不同于典型数据集包含来自实验室环境的图像，PlantSeg 主要由野外植物病害图像组成。这一选择提升了实际适用性，因为训练后的模型可用于综合病害管理。（3）规模：PlantSeg 规模庞大，包含 11,400 张带有病害分割掩码的图像，以及另外 8,000 张按植物类型分类的健康植物图像。大量技术实验验证了 PlantSeg 标注的高质量。该数据集不仅使研究人员能够评估其图像分类方法，还为开发和基准测试先进的植物病害分割算法提供了关键基础。
英文摘要：
Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image segmentation is usually adopted for precisely identifying diseased regions, thereby advancing precision agriculture. Developing robust image segmentation models for plant diseases demands high-quality annotations across numerous images. However, existing plant disease datasets typically lack segmentation labels and are often confined to controlled laboratory settings, which do not adequately reflect the complexity of natural environments. Motivated by this fact, we established PlantSeg, a large-scale segmentation dataset for plant diseases. PlantSeg distinguishes itself from existing datasets in three key aspects. (1) Annotation type: Unlike the majority of existing datasets that only contain class labels or bounding boxes, each image in PlantSeg includes detailed and high-quality segmentation masks, associated with plant types and disease names. (2) Image source: Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. This choice enhances the practical applicability, as the trained models can be applied for integrated disease management. (3) Scale: PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type. Extensive technical experiments validate the high quality of PlantSeg's annotations. This dataset not only allows researchers to evaluate their image classification methods but also provides a critical foundation for developing and benchmarking advanced plant disease segmentation algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
