The 1st Agriculture-Vision Challenge: Methods and Results

Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Humphrey Shi, Yunchao Wei, Zilong Huang, Alexander G. Schwing

Abstract

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 $\color{OrangeRed}{here}$.

Locations

• Munin Open Research Archive (The Arctic University of Norway) - View - PDF
• arXiv (Cornell University) - View - PDF
• 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) - View