{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:56:10Z","timestamp":1775598970295,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>\n      \n        Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts. A central challenge is yield estimation, i.e., predicting crop yields before harvest. We introduce a scalable, accurate, and inexpensive method to predict crop yields using publicly available remote sensing data. Our approach improves existing techniques in three ways. First, we forego hand-crafted features traditionally used in the remote sensing community and propose an approach based on modern representation learning ideas. We also introduce a novel dimensionality reduction technique that allows us to train a Convolutional Neural Network or Long-short Term Memory network and automatically learn useful features even when labeled training data are scarce. Finally, we incorporate a Gaussian Process component to explicitly model the spatio-temporal structure of the data and further improve accuracy. We evaluate our approach on county-level soybean yield prediction in the U.S. and show that it outperforms competing techniques.\n      \n    <\/jats:p>","DOI":"10.1609\/aaai.v31i1.11172","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T17:19:50Z","timestamp":1656091190000},"source":"Crossref","is-referenced-by-count":314,"title":["Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data"],"prefix":"10.1609","volume":"31","author":[{"given":"Jiaxuan","family":"You","sequence":"first","affiliation":[]},{"given":"Xiaocheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Melvin","family":"Low","sequence":"additional","affiliation":[]},{"given":"David","family":"Lobell","sequence":"additional","affiliation":[]},{"given":"Stefano","family":"Ermon","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2017,2,12]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/11172\/11031","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/11172\/11031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T17:19:51Z","timestamp":1656091191000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/11172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,12]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,2,11]]}},"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1609\/aaai.v31i1.11172","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2017,2,12]]}}}