# FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data

arXiv: https://arxiv.org/abs/2509.06907 · HTML: https://mlsanity.com/paper/2509.06907/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2025.

## Built on by (most cited first, 4 of 4)

- [SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping](https://mlsanity.com/paper/2603.27519/) (2026)
- [3D Reconstruction and Knowledge Distillation to Improve Multi-View Image Models to Explore Spike Volume Estimation in Wheat](https://mlsanity.com/paper/2605.20940/) (2026)
- [PhenoNEST: A Neuro-Symbolic Framework for Ontology-Aware Multimodal Plant Phenotyping and Trait Discovery](https://mlsanity.com/paper/2607.03245/) (2026)
- [RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery](https://mlsanity.com/paper/2609.25567/) (2026)

## Abstract (arXiv)

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/.