Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data
构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。
← 主题
#氮素监测
Predicting Wetland Nitrogen Levels Through Microbial Signatures: A Taxonomy-Based Approach Validated by Machine Learning
利用湿地微生物群落特征结合机器学习预测土壤总氮水平。
Optimizing canopy nitrogen retrieval in wheat from high-resolution UAV hyperspectral data using RTM-based Gaussian process regression
用PROSAIL-PRO与高斯过程回归从无人机高光谱数据反演小麦冠层氮含量。
UAV-based rice nitrogen status monitoring using machine learning and deep learning
UAV-based rice nitrogen status monitoring using machine learning and deep learning。Precision Agriculture
DMP-UNet: a multimodal fusion network for high-precision nitrogen status monitoring in wheat from UAV multispectral images
提出多模态融合网络DMP-UNet，利用无人机多光谱图像高精度监测小麦氮素水平。
A physics-guided hybrid framework for peanut nitrogen monitoring (Computers and Electronics in Agriculture)
提出物理-数据融合框架POSNH，用于花生冠层氮素含量遥感监测，降低样本依赖并提高泛化能力。
基于无人机多源遥感特征的茶树生长与氮素状况深度学习监测
融合无人机多源遥感特征与深度学习，高精度估算茶树生物量与氮素积累量。
Assessing rice genotype dependency in remote sensing: challenges in nitrogen discrimination and yield forecasting
评估遥感模型在不同水稻基因型间的可转移性，用于氮肥监测和产量预测。