# Ortho-Fuse: Orthomosaic Generation for Sparse High-Resolution Crop Health Maps Through Intermediate Optical Flow Estimation

arXiv: https://arxiv.org/abs/2510.10360 · HTML: https://mlsanity.com/paper/2510.10360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 55 references that are in the map.
- First posted on arXiv 11 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis](https://mlsanity.com/paper/2001.01306/) (2020)
- [CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model](https://mlsanity.com/paper/2403.11614/) (2024)
- [MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation](https://mlsanity.com/paper/2405.13570/) (2024)
- [UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable Sets](https://mlsanity.com/paper/2403.05086/) (2024)
- [On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models](https://mlsanity.com/paper/2411.03177/) (2024)
- [Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey](https://mlsanity.com/paper/2006.11391/) (2020)
- [APT: Adaptive Personalized Training for Diffusion Models with Limited Data](https://mlsanity.com/paper/2507.02687/) (2025)
- [Deep Learning Improvements for Sparse Spatial Field Reconstruction](https://mlsanity.com/paper/2408.12531/) (2024)
- [Artificial Intelligence for Digital Agriculture at Scale: Techniques, Policies, and Challenges](https://mlsanity.com/paper/2001.09786/) (2020)
- [1st Place Solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction](https://mlsanity.com/paper/2404.10441/) (2024)

## Abstract (arXiv)

AI-driven crop health mapping systems offer substantial advantages over conventional monitoring approaches through accelerated data acquisition and cost reduction. However, widespread farmer adoption remains constrained by technical limitations in orthomosaic generation from sparse aerial imagery datasets. Traditional photogrammetric reconstruction requires 70-80% inter-image overlap to establish sufficient feature correspondences for accurate geometric registration. AI-driven systems operating under resource-constrained conditions cannot consistently achieve these overlap thresholds, resulting in degraded reconstruction quality that undermines user confidence in autonomous monitoring technologies. In this paper, we present Ortho-Fuse, an optical flow-based framework that enables the generation of a reliable orthomosaic with reduced overlap requirements. Our approach employs intermediate flow estimation to synthesize transitional imagery between consecutive aerial frames, artificially augmenting feature correspondences for improved geometric reconstruction. Experimental validation demonstrates a 20% reduction in minimum overlap requirements. We further analyze adoption barriers in precision agriculture to identify pathways for enhanced integration of AI-driven monitoring systems.