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

This study demonstrates the effectiveness of unmanned aerial vehicles (UAVs) multispectral imagery for enhancing leaf nitrogen content estimation in common bean (Phaseolus vulgaris L.) through the integration of vegetation indices (VIs) and texture features. Research conducted over two years (2021–2022) evaluated various nitrogen rates across critical growth stages (V4, R5, and R7). The findings contribute to sustainable agricultural practices by optimizing nitrogen use efficiency (NUE) and minimizing environmental impacts. Machine learning models combining spectral and textural information significantly outperformed single-index approaches, achieving root mean square error (RMSE) values of 1.80 g kg^-1 (relative root mean square error – RRMSE = 2.93%) at V4 stage using support vector machine with VIs, and 2.79 g kg^-1 (RRMSE = 5.20%) at R5 stage using random forest with VIs. For later growth stages (R7) and across the entire season (all growth stages), the combination of VIs and texture metrics proved most effective, with random forest achieving RMSE values of 3.42 and 3.96 g kg^-1 (RRMSE = 7.40 and 7.32%), respectively. Texture analysis in across-row directions (90° and 135°) provided superior performance compared to traditional diagonal approaches for row-planted crops. Linear regression analysis showed that normalized difference texture indices incorporating correlation and homogeneity explained up to 71% of leaf nitrogen content variability at R7 stage. This research also underscores the critical role of precision agriculture technologies in enhancing soil health and promoting sustainable farming systems. By optimizing nitrogen application, the study contributes to improved nutrient management practices that not only increase crop yield but also reduce the environmental footprint of agricultural production. Excessive nitrogen use is a major contributor to soil acidification, greenhouse gas emissions, and water contamination; thus, a more efficient nitrogen application system can mitigate these challenges, supporting both climate change adaptation and mitigation. The optimal nitrogen rate of 91 kg ha^-1, validated through both yield response and leaf nitrogen measurements, provides a robust benchmark for nitrogen management in common bean production. This methodology offers a practical framework for real-time, site-specific nitrogen management, which not only improves current recommendation systems but also contributes to overall ecosystem health and biodiversity by promoting soil resilience and reducing harmful agricultural inputs. Furthermore, it aligns with modern agricultural practices that emphasize optimal resource use while preserving long-term soil fertility, ensuring food security through enhanced crop productivity.