Joint estimation of potato yield and nitrogen status using UAV-derived spectral and structural data

Understanding how canopy structure and plant nutritional status jointly regulate crop productivity remains a central challenge for precision agriculture, particularly when observations are limited to single growth stages. This study examines whether three-dimensional canopy information derived from unmanned aerial vehicle (UAV) LiDAR can be integrated with multispectral observations to improve spatial characterization of potato yield potential and nitrogen status under irrigated Prairie conditions.

Multispectral imagery and high-density UAV-LiDAR data were acquired at row closure across two growing seasons in southwestern Manitoba, Canada, spanning a controlled gradient of nitrogen availability. Rather than treating yield and nitrogen status as independent targets, we evaluated a joint learning framework in which both variables were estimated simultaneously from the same fused feature space. Multiple neural network architectures were compared under identical data partitions to isolate the effects of shared representation learning. Model interpretation was performed using attribution analysis to distinguish spectral versus structural feature dependence.

Joint learning substantially altered model behaviour. Yield estimation, which proved weak when optimized in isolation, improved markedly when trained alongside nitrogen status, indicating that shared canopy representations capture integrative growth signals not accessible through yield-only optimization. In contrast, nitrogen prediction exhibited limited or inconsistent benefit from joint learning, remaining primarily governed by chlorophyll-sensitive spectral information. Attribution results revealed that yield relied on a broader combination of spectral responses and LiDAR-derived structural descriptors, including canopy height distribution, volumetric development, and spatial heterogeneity, whereas nitrogen status remained physiologically localized within the spectral domain.

These results demonstrate that canopy structure provides complementary information for cumulative traits such as yield, even from single-date acquisitions, while offering limited leverage for physiologically proximal indicators like nitrogen concentration. More broadly, the study shows that multi-task learning does not uniformly enhance prediction accuracy but instead exposes how different agronomic traits are encoded across spectral and structural dimensions. This has direct implications for designing UAV-based decision support systems, where aligning sensing modalities, learning strategy, and crop physiology is critical for meaningful inference.