Optimizing nitrogen estimates in common bean canopies throughout key growth stages via spectral and textural data from unmanned aerial vehicle (UAV) multispectral imagery

This study investigates the potential of utilizing multispectral imagery acquired from unmanned aerial vehicles (UAVs) to enhance the accuracy of leaf nitrogen content (LNC) estimation, a crucial parameter for assessing crop nitrogen status and guiding nitrogen management practices. We integrated selected vegetation indices (VIs) and texture data (gray level co-occurrence matrix - GLCM) derived from UAV-based multispectral images to estimate LNC in common bean (Phaseolus vulgaris L.). Therefore, the objectives of this study were (i) to determine the optimal VIs and texture metrics from UAV multispectral imagery for estimating LNC, and (ii) to explore the capability of integrating spectral and textural information in the improvement of N status monitoring.