{"ID":2873554,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06907","arxiv_id":"2509.06907","title":"FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data","abstract":"Vision-driven field monitoring is central to digital agriculture, yet models built on general-domain pretrained backbones often fail to generalize across tasks, owing to the interaction of fine, variable canopy structures with fluctuating field conditions. We present FoMo4Wheat, one of the first crop-domain vision foundation model pretrained with self-supervision on ImAg4Wheat, the largest and most diverse wheat image dataset to date (2.5 million high-resolution images collected over a decade at 30 global sites, spanning \u003e2,000 genotypes and \u003e500 environmental conditions). This wheat-specific pretraining yields representations that are robust for wheat and transferable to other crops and weeds. Across ten in-field vision tasks at canopy and organ levels, FoMo4Wheat models consistently outperform state-of-the-art models pretrained on general-domain dataset. These results demonstrate the value of crop-specific foundation models for reliable in-field perception and chart a path toward a universal crop foundation model with cross-species and cross-task capabilities. FoMo4Wheat models and the ImAg4Wheat dataset are publicly available online: https://github.com/PheniX-Lab/FoMo4Wheat and https://huggingface.co/PheniX-Lab/FoMo4Wheat. The demonstration website is: https://fomo4wheat.phenix-lab.com/.","short_abstract":"Vision-driven field monitoring is central to digital agriculture, yet models built on general-domain pretrained backbones often fail to generalize across tasks, owing to the interaction of fine, variable canopy structures with fluctuating field conditions. We present FoMo4Wheat, one of the first crop-domain vision foun...","url_abs":"https://arxiv.org/abs/2509.06907","url_pdf":"https://arxiv.org/pdf/2509.06907v1","authors":"[\"Bing Han\",\"Chen Zhu\",\"Dong Han\",\"Rui Yu\",\"Songliang Cao\",\"Jianhui Wu\",\"Scott Chapman\",\"Zijian Wang\",\"Bangyou Zheng\",\"Wei Guo\",\"Marie Weiss\",\"Benoit de Solan\",\"Andreas Hund\",\"Lukas Roth\",\"Kirchgessner Norbert\",\"Andrea Visioni\",\"Yufeng Ge\",\"Wenjuan Li\",\"Alexis Comar\",\"Dong Jiang\",\"Dejun Han\",\"Fred Baret\",\"Yanfeng Ding\",\"Hao Lu\",\"Shouyang Liu\"]","published":"2025-09-08T17:23:28Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","project_urls":"[\"https://fomo4wheat.phenix-lab.com/\"]","has_code":false,"code_links":[{"ID":610066,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2873554,"paper_url":"https://arxiv.org/abs/2509.06907","paper_title":"FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data","repo_url":"https://github.com/PheniX-Lab/FoMo4Wheat","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}