Akademik Veri Yönetim Sistemi
Expert Systems, cilt.42, sa.10, 2025 (SCI-Expanded)
Driven by the dual pressures of rapid global population growth and escalating climate change, there is a growing demand for real-time monitoring of crop nitrogen levels to support precision agriculture. This necessity has catalysed the integration of crop modelling techniques with remote sensing technologies. Addressing challenges such as multi-source remote sensing data heterogeneity and limited generalisation in nitrogen inversion models for cotton canopies, this paper designs a novel inversion framework based on the assimilation of diverse remote sensing sources and mechanistic crop models. Firstly, this paper employed spectral resampling techniques, fuzzy logic for uncertainty quantification, and Pearson correlation analysis to harmonise differences in spectral characteristics and spatial resolution between Sentinel-2A and Landsat 8 imagery, ultimately identifying eight nitrogen-sensitive features. Subsequently, a multi-scale feature enhancement module was developed to improve representational richness. Additionally, the paper employed a satellite image fusion module, which effectively reduced data heterogeneity errors by 12.7% across sources. Building on this, a hybrid GNN-EnKF model was proposed. GNN was used to establish spatial neighbourhood dependencies, while EnKF dynamically adjusted the parameters within the WOFOST crop model. This approach successfully fuses data-driven learning with physically based modelling. Experimental evaluations revealed that the proposed architecture attained a mAP of 95.83%, outperforming baseline models such as ResNet18 (83.92%) and Transformer (92.84%), demonstrating robust adaptability in complex agricultural settings. In conclusion, the framework presented in this paper offers a high-accuracy nitrogen monitoring solution tailored for precision farming, and provides strong data support for cotton nitrogen deficiency and additional fertilisation.