arXiv:2511.16031v1 Announce Type: new 
Abstract: Recent advances in plant phenotyping have driven widespread adoption of multi sensor platforms for collecting crop canopy reflectance data. This includes the collection of heterogeneous data across multiple platforms, with Unmanned Aerial Vehicles (UAV) seeing significant usage due to their high performance in crop monitoring, forecasting, and prediction tasks. Similarly, satellite missions have been shown to be effective for agriculturally relevant tasks. In contrast to UAVs, such missions are bound to the limitation of spatial resolution, which hinders their effectiveness for modern farming systems focused on micro-plot management. In this work, we propose a cross modal learning strategy that enriches high-resolution satellite imagery with UAV level visual detail for crop canopy trait estimation. Using a dataset of approximately co registered satellite UAV image pairs collected from replicated plots of 84 hybrid maize varieties across five distinct locations in the U.S. Corn Belt, we train a model that learns fine grained spectral spatial correspondences between sensing modalities. Results show that the generated UAV-like representations from satellite inputs consistently outperform real satellite imagery on multiple downstream tasks, including yield and nitrogen prediction, demonstrating the potential of cross-modal correspondence learning to bridge the gap between satellite and UAV sensing in agricultural monitoring.

أدت التطورات الأخيرة في تصنيف النباتات إلى زيادة استخدام المنصات متعددة المستشعرات لجمع بيانات انعكاس مظلة المحاصيل. تعتبر الطائرات بدون طيار (UAV) فعالة بشكل خاص في مراقبة المحاصيل، بينما تواجه المهام الفضائية قيودًا في الدقة المكانية. تقترح هذه الدراسة استراتيجية تعلم عبر الأنماط تجمع بين الصور الفضائية عالية الدقة مع تفاصيل على مستوى UAV لتحسين تقدير خصائص مظلة المحاصيل، باستخدام مجموعة بيانات من 84 نوعًا هجينًا من الذرة.

Los avances recientes en fenotipado de plantas han impulsado la adopción generalizada de plataformas multi-sensoras para la recolección de datos de reflectancia del dosel de cultivos. Los Vehículos Aéreos No Tripulados (UAV) son especialmente efectivos para el monitoreo de cultivos, mientras que las misiones satelitales enfrentan limitaciones en la resolución espacial. Este trabajo propone una estrategia de aprendizaje cross-modal que enriquece las imágenes satelitales de alta resolución con detalles visuales a nivel de UAV para la estimación de rasgos del dosel de cultivos, utilizando un conj…

Des avancées récentes en phénotypage des plantes ont conduit à une adoption accrue de plateformes multi-capteurs pour la collecte de données de réflectance du couvert végétal. Les véhicules aériens sans pilote (UAV) sont particulièrement efficaces pour le suivi des cultures, tandis que les missions satellites rencontrent des limitations en termes de résolution spatiale. Cette étude propose une stratégie d'apprentissage cross-modal qui combine des images satellites haute résolution avec des détails de niveau UAV pour améliorer l'estimation des traits du couvert végétal, en utilisant des données…

Recent advancements in plant phenotyping have led to the increased use of multi-sensor platforms for collecting crop canopy reflectance data. Unmanned Aerial Vehicles (UAVs) are particularly effective for crop monitoring, while satellite missions face limitations in spatial resolution. This study proposes a cross-modal learning strategy that combines high-resolution satellite imagery with UAV-level detail to enhance crop canopy trait estimation, utilizing data from 84 hybrid maize varieties.

Crossmodal learning for Crop Canopy Trait Estimation

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