  The first Agriculture-Vision Challenge aims to encourage research in
developing novel and effective algorithms for agricultural pattern recognition
from aerial images, especially for the semantic segmentation task associated
with our challenge dataset. Around 57 participating teams from various
countries compete to achieve state-of-the-art in aerial agriculture semantic
segmentation. The Agriculture-Vision Challenge Dataset was employed, which
comprises of 21,061 aerial and multi-spectral farmland images. This paper
provides a summary of notable methods and results in the challenge. Our
submission server and leaderboard will continue to open for researchers that
are interested in this challenge dataset and task; the link can be found here.
