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The success of deep learning in visual recognition tasks has drivenadvancements in multiple fields of research. Particularly, increasing attentionhas been drawn towards its application in agriculture. Nevertheless, whilevisual pattern recognition on farmlands carries enormous economic values,little progress has been made to merge computer vision and crop sciences due tothe lack of suitable agricultural image datasets. Meanwhile, problems inagriculture also pose new challenges in computer vision. For example, semanticsegmentation of aerial farmland images requires inference over extremelylarge-size images with extreme annotation sparsity. These challenges are notpresent in most of the common object datasets, and we show that they are morechallenging than many other aerial image datasets. To encourage research incomputer vision for agriculture, we present Agriculture-Vision: a large-scaleaerial farmland image dataset for semantic segmentation of agriculturalpatterns. We collected 94,986 high-quality aerial images from 3,432 farmlandsacross the US, where each image consists of RGB and Near-infrared (NIR)channels with resolution as high as 10 cm per pixel. We annotate nine types offield anomaly patterns that are most important to farmers. As a pilot study ofaerial agricultural semantic segmentation, we perform comprehensive experimentsusing popular semantic segmentation models; we also propose an effective modeldesigned for aerial agricultural pattern recognition. Our experimentsdemonstrate several challenges Agriculture-Vision poses to both the computervision and agriculture communities. Future versions of this dataset willinclude even more aerial images, anomaly patterns and image channels. Moreinformation at https://www.agriculture-vision.com.

Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis