Abstract
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.
Abstract (translated)
AI驱动的农作物健康地图系统通过加速数据获取和降低成本，相对于传统监测方法提供了显著的优势。然而，由于从稀疏的航空影像数据集中生成正射镶嵌图的技术限制，这些系统的广泛农民采用受到了阻碍。传统的摄影测量重建需要70-80%的照片间重叠度来建立足够的特征对应关系以实现准确的几何配准。在资源受限条件下运行的AI系统无法持续达到这种重叠阈值，导致重建质量下降，损害了用户对自主监测技术的信心。 本文提出了Ortho-Fuse框架，这是一个基于光学流的方法，可以在减少重叠要求的情况下生成可靠的正射镶嵌图。我们的方法利用中间流量估计来合成连续航拍帧之间的过渡影像，从而人工增加特征对应关系以改善几何重建。实验验证表明，这可以将最低重叠度需求降低20%。 我们进一步分析了精准农业中的采用障碍，并确定了AI驱动监测系统集成的改进路径。
URL
https://arxiv.org/abs/2510.10360
