The 1st Agriculture-Vision Challenge: Methods and Results
It addresses the problem of automated agricultural monitoring for farmers and researchers, but is incremental as it builds on existing semantic segmentation methods applied to a new domain-specific dataset.
The paper presents the first Agriculture-Vision Challenge, which tackled agricultural pattern recognition from aerial images using a dataset of 21,061 multi-spectral farmland images, with 57 teams competing to achieve state-of-the-art results in semantic segmentation.
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.