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.
@article{arxiv.2004.09754,
title = {The 1st Agriculture-Vision Challenge: Methods and Results},
author = {Mang Tik Chiu and Xingqian Xu and Kai Wang and Jennifer Hobbs and Naira Hovakimyan and Thomas S. Huang and Honghui Shi and Yunchao Wei and Zilong Huang and Alexander Schwing and Robert Brunner and Ivan Dozier and Wyatt Dozier and Karen Ghandilyan and David Wilson and Hyunseong Park and Junhee Kim and Sungho Kim and Qinghui Liu and Michael C. Kampffmeyer and Robert Jenssen and Arnt B. Salberg and Alexandre Barbosa and Rodrigo Trevisan and Bingchen Zhao and Shaozuo Yu and Siwei Yang and Yin Wang and Hao Sheng and Xiao Chen and Jingyi Su and Ram Rajagopal and Andrew Ng and Van Thong Huynh and Soo-Hyung Kim and In-Seop Na and Ujjwal Baid and Shubham Innani and Prasad Dutande and Bhakti Baheti and Sanjay Talbar and Jianyu Tang},
journal= {arXiv preprint arXiv:2004.09754},
year = {2020}
}