Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts. The original dataset affiliated with the 2020 CVPR paper includes 94,986 512x512images sampled from 3,432 farmlands with nine types of annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster. All of these patterns have substantial impacts on field conditions and the final yield. These farmland images were captured between 2017 and 2019 across multiple growing seasons in numerous farming locations in the US. Each field image contains four color channels: Near-infrared (NIR), Red, Green and Blue. We first randomly split the 3,432 farmland images with a 6/2/2 train/val/test ratio. We then assign each sampled image to the split of the farmland image they are cropped from. This guarantees that no cropped images from the same farmland will appear in multiple splits in the final dataset. The generated (supervised) Agriculture-Vision dataset thus contains 56,944/18,334/19,708 train/val/test images. Additionally, we continue to grow this datset. In 2021 as a part of the Prize Challenge at CVPR, we have added sequences of full-field imagery across 52 fields to promote the use of weakly supervised methods.
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aws s3 ls s3://intelinair-data-releases/agriculture-vision/cvpr_paper_2020/ --no-sign-request
aws s3 ls s3://intelinair-data-releases/agriculture-vision/cvpr_challenge_2021/ --no-sign-request
aws s3 ls s3://intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full/ --no-sign-request