UAV hyperspectral remote sensing for crop nitrogen monitoring: progress, challenges, and perspectives

作者：Kang Yu, Anirudh Belwalkar, Wuhua Wang, Yuncai Hu, Addisu Hunegnaw, Abdul Nurunnabi, Thorsten Ruf, Fei Li, Liangliang Jia, Lammert Kooistra, Yuxin Miao, Felix Norman Teferle · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.101507 · 被引用次数：11 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Remote Sensing and Land Use

Nitrogen (N) is the most important macro nutrient for crop productivity. However, precise N input balancing productivity and environmental footprint is always challenging due the difficulty of measuring plant N status in a timely manner. Uncrewed aerial vehicles (UAVs) and hyperspectral imaging (HSI) have revolutionized smart vegetation monitoring, offering unprecedented capabilities for mapping plant traits and precision crop nutrient management. This review evaluates recent advancements in crop N monitoring, highlights key challenges in N prediction, and provides recommendations for improving UAV-based HSI to support decision-making and enhance N use efficiency. Our analysis reveals that traditional machine learning (ML) methods have surpassed the vegetation index-based regression methods in N prediction. However, most studies report significant gaps between model training accuracy and validation performance, with independent testing being insufficiently addressed in the literature. Some research highlighted the integration of solar-induced chlorophyll fluorescence (SIF) and plant traits to enhance N prediction by leveraging narrow-band and sub-nanometer resolution hyperspectral imagery. Additionally, we discuss the challenges of disentangling phenological and physiological effects from canopy structural and biochemical properties in hyperspectral analysis. Addressing these confounding factors through advanced modeling techniques like deep neural networks will significantly...