{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T05:00:38Z","timestamp":1785128438611,"version":"3.55.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007414","name":"Qassim University","doi-asserted-by":"publisher","award":["QU-APC-2026"],"award-info":[{"award-number":["QU-APC-2026"]}],"id":[{"id":"10.13039\/501100007414","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1883297","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T04:26:03Z","timestamp":1785126363000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A multimodal geospatial foundation model anticipates crop stress and yield failure across climates and species"],"prefix":"10.3389","volume":"17","author":[{"given":"Saleh","family":"Albahli","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University","place":["Buraydah, Saudi Arabia"]},{"name":"Department of Computer Science, Kent State University","place":["Kent, OH, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Shiraz","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Urdu University of Arts, Science and Technology","place":["Islamabad, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"B27","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1561\/2200000101","article-title":"Conformal prediction: a gentle introduction","volume":"16","author":"Angelopoulos","year":"2023","journal-title":"Found. 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