# The 1st Agriculture-Vision Challenge: Methods and Results

arXiv: https://arxiv.org/abs/2004.09754 · HTML: https://mlsanity.com/paper/2004.09754/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications](https://mlsanity.com/paper/2312.17016/) (2023)
- [Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis](https://mlsanity.com/paper/2303.02460/) (2023)
- [GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing](https://mlsanity.com/paper/2307.14612/) (2023)
- [Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification](https://mlsanity.com/paper/2203.06041/) (2022)
- [Rail-5k: a Real-World Dataset for Rail Surface Defects Detection](https://mlsanity.com/paper/2106.14366/) (2021)
- [Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery](https://mlsanity.com/paper/2104.10249/) (2021)
- [XAI for time-series classification leveraging image highlight methods](https://mlsanity.com/paper/2311.17110/) (2023)
- [Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling](https://mlsanity.com/paper/2404.08931/) (2024)
- [Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks](https://mlsanity.com/paper/2111.03845/) (2021)
- [Rank-Aware Quantile Activation for Motion-Robust Crop Segmentation in UAV Imagery](https://mlsanity.com/paper/2606.01118/) (2026)

## Abstract (arXiv)

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.