{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-gaussian-process-for-crop-yield","title":"Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data","arxiv_id":null,"date":"2017-02-12","proceeding":"AAAI 2017 2017 2","authors":["Jiaxuan You","Xiaocheng Li","Melvin Low","David Lobell","Stefano Ermon"],"abstract":"Agricultural monitoring, especially in developing countries,\r\ncan help prevent famine and support humanitarian efforts.\r\nA central challenge is yield estimation, i.e., predicting crop\r\nyields before harvest.\r\nWe introduce a scalable, accurate, and inexpensive method\r\nto predict crop yields using publicly available remote sensing data. Our approach improves existing techniques in three\r\nways. First, we forego hand-crafted features traditionally\r\nused in the remote sensing community and propose an approach based on modern representation learning ideas. We\r\nalso introduce a novel dimensionality reduction technique\r\nthat allows us to train a Convolutional Neural Network or\r\nLong-short Term Memory network and automatically learn\r\nuseful features even when labeled training data are scarce.\r\nFinally, we incorporate a Gaussian Process component to\r\nexplicitly model the spatio-temporal structure of the data\r\nand further improve accuracy. We evaluate our approach on\r\ncounty-level soybean yield prediction in the U.S. and show\r\nthat it outperforms competing techniques.","url_abs":"https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14435","url_pdf":"https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewFile/14435/14067","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-gaussian-process-for-crop-yield","repo_url":"https://github.com/JiaxuanYou/crop_yield_prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"crop-yield-prediction","task_name":"Crop Yield Prediction"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"humanitarian","task_name":"Humanitarian"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"},{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}