A major limitation in perennial orchard management is the lack of integrative frameworks that connect canopy nitrogen (N) dynamics with canopy structure, environmental drivers, yield formation, and postharvest quality at the individual-tree scale. This study presents a multi-sensor, tree-level framework combining unmanned aerial vehicle (UAV) multispectral imagery, LiDAR-derived canopy structure (e.g., height, canopy area, plant area index), and environmental variables (e.g., temperature, vapor pressure deficit, and evapotranspiration) within an explainable machine-learning approach to quantify leaf and canopy nitrogen content, yield, and fruit quality in avocado. The framework was evaluated across three contrasting systems: a controlled lysimetric gradient (Gilat; N10-N120 mg N L-& sup1;), a semi-arid commercial orchard (Kfar Menachem), and a humid commercial orchard (Kabri), using multi-temporal UAV acquisitions and ground measurements collected during 2022-2024. Random Forest models accurately predicted leaf nitrogen concentration (LNC; R & sup2; = 0.92/0.78/0.78 for calibration/validation/prediction) and canopy nitrogen content (CNC; R & sup2; = 0.90/0.76/0.73 for calibration/validation/prediction), indicating reliable scaling from leaf to canopy level. Yield prediction showed robust performance (R & sup2; = 0.90/0.75/0.71 for calibration/validation/prediction), with consistent results across sites. Postharvest quality models showed high accuracy (R & sup2; = 0.83-0.92), with shapley additive explanations (SHAP) analysis indicating that late-season nitrogen status, canopy structure, and meteorological conditions contributed more strongly to decay risk than fertilizer input alone. Nitrogen-use efficiency peaked at an intermediate nitrogen input (similar to N20), indicating a constrained optimal range in the controlled lysimetric experiment. The framework links nitrogen status, canopy structure, and environmental conditions with yield and fruit quality outcomes, supporting tree-scale diagnostics for improved nitrogen management, while highlighting the need for further validation across sites, seasons, and cultivars.