FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
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
公開日: 2025/9/8
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 >2,000 genotypes and >500 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/.