论文标题
图像标签工具和农业数据集用于深度学习
An Image Labeling Tool and Agricultural Dataset for Deep Learning
论文作者
论文摘要
我们介绍了一个标签工具和旨在促进农业计算机视觉研究的数据集。注释工具介绍了使用各种手动，半自动和完全自动的工具进行标记的新方法。该数据集包括从商业温室收集的原始图像，来自PlantVillage的图像以及Google图像的图像。图像用前景叶，水果和茎实例以及患病的叶片区域的分割注释。标签为扩展的可可格式。数据集总共包含10k西红柿，7k叶，2K茎和2K患病的叶子注释。
We introduce a labeling tool and dataset aimed to facilitate computer vision research in agriculture. The annotation tool introduces novel methods for labeling with a variety of manual, semi-automatic, and fully-automatic tools. The dataset includes original images collected from commercial greenhouses, images from PlantVillage, and images from Google Images. Images were annotated with segmentations for foreground leaf, fruit, and stem instances, and diseased leaf area. Labels were in an extended COCO format. In total the dataset contained 10k tomatoes, 7k leaves, 2k stems, and 2k diseased leaf annotations.
