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Agriculture: Difference between revisions
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== Background Readings ==
* [https://doi.org/10.1016/j.agsy.2020.103016 Machine learning for large-scale crop yield forecasting]
* ''[https://link.springer.com/article/10.1007/s11069-017-3106-x Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices]''
* [https://www.mdpi.com/2072-4292/11/6/676 Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review]
== Online Courses and Course Materials ==
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* '''Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis:''' A dataset composed of 94,986 aerial images from 3,432 farmlands across the United States. The images contains RGB channels and Near-infrared at a resolution of 10 cm per pixel. [https://www.agriculture-vision.com/dataset Dataset], and [https://arxiv.org/abs/2001.01306 paper] describing the dataset.
* LandCoverNet: A multispectral satellite imagery dataset acquired from Sentinel-2 and can be used for land cover classification. The dataset was created by the Radiant Earth Foundation. [http://registry.mlhub.earth/10.34911/rdnt.d2ce8i/ Dataset] and [https://radiant-mlhub.s3-us-west-2.amazonaws.com/landcovernet/Documentation.pdf documentation].
* '''CropHarvest''': The [https://openreview.net/forum?id=JtjzUXPEaCu CropHarvest dataset], compiled by researchers affiliated with NASA Harvest, provides a unified global dataset, API, and benchmarks for ML methods in crop-type mapping.
=== Kaggle datasets ===
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