{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T20:47:49Z","timestamp":1775767669037,"version":"3.50.1"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"12","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2020-67021-32855"],"award-info":[{"award-number":["2020-67021-32855"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2020-67021-32799"],"award-info":[{"award-number":["2020-67021-32799"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2021-67021-35329"],"award-info":[{"award-number":["2021-67021-35329"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2021-67021-35344"],"award-info":[{"award-number":["2021-67021-35344"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2023-67021-39829"],"award-info":[{"award-number":["2023-67021-39829"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["2231251"],"award-info":[{"award-number":["2231251"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["2332864"],"award-info":[{"award-number":["2332864"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["2437003"],"award-info":[{"award-number":["2437003"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["2528968"],"award-info":[{"award-number":["2528968"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Commun. ACM"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>Starting in 2020, the U.S. Department of Agriculture\u2019s National Institute of Food and Agriculture (NIFA) has funded five National Artificial Intelligence (AI) Research Institutes, in a close strategic partnership with the U.S. National Science Foundation (NSF). These AI institutes share a vision to initiate and propel the research, development, and deployment of AI tools and technologies within various sectors of agriculture, as well as to prepare the next generation of an AI-ready workforce in agricultural sectors. In this article, we report on the various use-inspired and foundational AI problems that these five AI Institutes (or \u201cAG-AI\u201d institutes, for short) are tackling, providing a sampler of technical outcomes as well as some of their broader impact highlights, including education, extension, and workforce development initiatives and community building. Collectively, these AI institutes are laying a strong foundation to usher in a new AI era for agriculture. The advances reported represent clear examples of using AI for societal good and for tacking the global grand challenge problem of securing the future of our food production. The advances also underscore the importance of sustaining and bolstering the U.S. strategic national federal investment in the AI institutes program, which has established the U.S. as a world leader in an area of enormous geopolitical as well as commercial importance.<\/jats:p>","DOI":"10.1145\/3760437","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T18:09:38Z","timestamp":1763575778000},"page":"78-86","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Advancing AI in Agriculture through Large-Scale Collaborative Research"],"prefix":"10.1145","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0760-9690","authenticated-orcid":false,"given":"Vikram","family":"Adve","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, Illinois, United States"}]},{"given":"Steve","family":"Brown","sequence":"additional","affiliation":[{"name":"University of California Davis, Davis, California, United States"}]},{"given":"Alan","family":"Fern","sequence":"additional","affiliation":[{"name":"Oregon State University, Corvallis, Oregon, United States"}]},{"given":"Baskar","family":"Ganapathysubramanian","sequence":"additional","affiliation":[{"name":"Iowa State University, Ames, Iowa, United States"}]},{"given":"Ananth","family":"Kalyanaraman","sequence":"additional","affiliation":[{"name":"Washington State University, Pullman, Washington, United States"}]},{"given":"Shashi","family":"Shekhar","sequence":"additional","affiliation":[{"name":"University of Minnesota Twin Cities, Minneapolis, Minnesota, United States"}]},{"given":"Ilias","family":"Tagkopoulos","sequence":"additional","affiliation":[{"name":"University of California Davis, Davis, California, United States"}]},{"given":"Jessica","family":"Wedow","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, Illinois, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/pnasnexus\/pgae575"},{"key":"e_1_3_1_3_2","unstructured":"Dongre V. et al. MIRAGE: A benchmark for multimodal information-seeking and reasoning in agricultural expert-guided conversations. arXiv:2506.20100 [cs.LG] (2025)."},{"key":"e_1_3_1_4_2","unstructured":"Fuglie K. et al. World agricultural output and productivity growth have slowed. U.S. Department of Agriculture Economic Research Service (2023); https:\/\/www.ers.usda.gov\/amber-waves\/2023\/december\/world-agricultural-output-and-productivity-growth-have-slowed"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/agj2.21353"},{"key":"e_1_3_1_6_2","unstructured":"Gauba A. et al. AgMMU: A comprehensive agricultural multimodal understanding and reasoning benchmark. arXiv:2504.10568 [cs.CV] (2025)."},{"key":"e_1_3_1_7_2","unstructured":"Gharsallaoui M.A. et al. Streamflow prediction with uncertainty quantification for water management: A constrained reasoning and learning approach. In Proceedings of the Intern. Joint Conf. on Artificial Intelligence (2024) 7269\u20137277."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i20.30210"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Islam M.R.U. et al. Self-attention-based diffusion model for time-series imputation in partial blackout scenarios. In Proceedings of the 39th AAAI Conf. on Artificial Intelligence (2025) 17564\u201317572.","DOI":"10.1609\/aaai.v39i17.33931"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106944"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3678717.3691261"},{"key":"e_1_3_1_12_2","unstructured":"Khokhar T. Chart: Globally 70% of freshwater is used for agriculture World Bank Blogs (2017); https:\/\/blogs.worldbank.org\/en\/opendata\/chart-globally-70-freshwater-used-agriculture"},{"key":"e_1_3_1_13_2","unstructured":"Khosravi M. et al. AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning. arXiv:2409.00735 (2024)."},{"key":"e_1_3_1_14_2","unstructured":"Kimara E. et al. AgriField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel. arXiv:2503.07813 (2025)."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43860-5"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1111\/gcb.16269"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-024-10207-z"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/gcb.16632"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2023.08.001"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i13.26865"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Shen Y. et al. WeedNet: A foundation model-based global-to-local AI approach for real-time weed species identification and classification. arXiv:2505.18930 (2025).","DOI":"10.21203\/rs.3.rs-7435620\/v1"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1002\/ppp3.10613"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Sivakumar A.N. et al. Demonstrating CropFollow++: Robust under canopy navigation with keypoints. In Proceedings of Robotics: Science and Systems (2024); https:\/\/www.roboticsproceedings.org\/rss20\/p023.pdf","DOI":"10.15607\/RSS.2024.XX.023"},{"key":"e_1_3_1_24_2","unstructured":"Solow W. et al. WOFOSTGym: A crop simulator for learning annual and perennial crop management strategies. In Proceedings of the Reinforcement Learning Conf. (2025)."},{"key":"e_1_3_1_25_2","unstructured":"Suomi-Lecker L. Cover crop and no-till management case study: Hewett Farm. University of Maine Cooperative Extension (2018); https:\/\/extension.umaine.edu\/agriculture\/soil-health\/no-till-and-reduced-tillage\/cover-crop-and-no-till-management-case-study-hewett-farm\/"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1002\/aaai.12164"},{"key":"e_1_3_1_27_2","volume-title":"The Farm Labor Problem: A Global Perspective 1st Ed","author":"Taylor J.E.","year":"2018","unstructured":"Taylor, J.E. and Charlton, D. The Farm Labor Problem: A Global Perspective 1st Ed. Academic Press (2018)."},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","unstructured":"Thapa K.K. et al. Attention-based models for snow-water equivalent prediction. In Proceedings of the 38th AAAI Conf. on Artificial Intelligence (2024) 22969\u201322975.","DOI":"10.1609\/aaai.v38i21.30337"},{"key":"e_1_3_1_29_2","unstructured":"USDA NRCS and Colorado State University. Estimate your whole farm and ranch carbon sequestration and greenhouse gas emissions using COMET-Farm (2025); https:\/\/comet-farm.com\/home"},{"key":"e_1_3_1_30_2","unstructured":"World Meteorological Organization. WMO report highlights growing shortfalls and stress in global water resources (2024); https:\/\/wmo.int\/media\/news\/wmo-report-highlights-growing-shortfalls-and-stress-global-water-resources"},{"key":"e_1_3_1_31_2","first-page":"102101","article-title":"Biotrove: A large curated image dataset enabling AI for biodiversity","volume":"37","author":"Yang C.H.","year":"2024","unstructured":"Yang, C.H. et al. Biotrove: A large curated image dataset enabling AI for biodiversity. Advances in Neural Information Processing Systems 37 (2024), 102101\u2013102120.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2025.102019"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9982017"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109072"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29993-z"},{"key":"e_1_3_1_36_2","unstructured":"Zaremehrjerdi H. et al. Towards large reasoning models for agriculture. arXiv preprint arXiv:2505.19259 (2025)."}],"container-title":["Communications of the ACM"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3760437","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T19:56:40Z","timestamp":1764100600000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3760437"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,25]]},"references-count":35,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1145\/3760437"],"URL":"https:\/\/doi.org\/10.1145\/3760437","relation":{},"ISSN":["0001-0782","1557-7317"],"issn-type":[{"value":"0001-0782","type":"print"},{"value":"1557-7317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,25]]},"assertion":[{"value":"2025-07-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}