  The success of deep learning in visual recognition tasks has driven
advancements in multiple fields of research. Particularly, increasing attention
has been drawn towards its application in agriculture. Nevertheless, while
visual pattern recognition on farmlands carries enormous economic values,
little progress has been made to merge computer vision and crop sciences due to
the lack of suitable agricultural image datasets. Meanwhile, problems in
agriculture also pose new challenges in computer vision. For example, semantic
segmentation of aerial farmland images requires inference over extremely
large-size images with extreme annotation sparsity. These challenges are not
present in most of the common object datasets, and we show that they are more
challenging than many other aerial image datasets. To encourage research in
computer vision for agriculture, we present Agriculture-Vision: a large-scale
aerial farmland image dataset for semantic segmentation of agricultural
patterns. We collected 94,986 high-quality aerial images from 3,432 farmlands
across 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 of
field anomaly patterns that are most important to farmers. As a pilot study of
aerial agricultural semantic segmentation, we perform comprehensive experiments
using popular semantic segmentation models; we also propose an effective model
designed for aerial agricultural pattern recognition. Our experiments
demonstrate several challenges Agriculture-Vision poses to both the computer
vision and agriculture communities. Future versions of this dataset will
include even more aerial images, anomaly patterns and image channels. More
information at https://www.agriculture-vision.com.
