Drone Remote Sensing
Our recently accepted two papers by Wang J et al. (2025) and Wang F et al. (2025) investigated drone remote sensing data-driven models for crop nitrogen estimation. Both works utilize drone remote sensing technology to explore nitrogen dynamics in wheat, aiming to enhance nitrogen use efficiency (NUE) in agriculture.
The Yin et al. 2025 paper aims to understand which types of features extracted from drone RGB images predict potato aboveground biomass (AGB) more accurately across different growth stages. The innovation lies in the approach of integrating color indices with the gray-level co-occurrence matrix (GLCM) and Gabor wavelet textures. Our results demonstrated that combining the color (spectral) features and texture features could significantly enhance predictive accuracy, whereas different features varied a lot in their contributions to model performance.
