Nitrogen (N) deficiency remains a major constraint on cereal productivity because it reduces chlorophyll formation, canopy photosynthesis, and grain filling, while blanket fertilizer practices often fail to match within-field variability and reduce nitrogen use efficiency (NUE) (Govindasamy et al., 2023). Although destructive sampling and laboratory diagnostics are accurate, they are slow and difficult to scale for timely, spatially targeted decisions in real farms (Fu et al., 2021). This study frames N deficiency detection as a cross-domain transfer learning problem and develops a cross-crop machine learning framework for wheat, maize, and rice using RGB imagery under field conditions. We harmonized and profiled three public datasets (wheat: 1,381 leaf images; maize: 1,200 canopy/plot images; rice: 1,500 leaf images with Leaf Color Chart-based labeling), applied standardized preprocessing, and trained baseline CNN and fine-tuned ResNet models with fixed random seeds and identical train/validation/test splits for reproducibility. Performance was evaluated under three scenarios: within-crop testing, direct cross-crop transfer without retraining, and domain adaptation using unlabeled target data. Four adaptation methods were benchmarked: CORAL, MMD, AdaBN, and Domain-Adversarial Neural Networks (DANN) (Ganin et al., 2016; Gretton et al., 2012; Li et al., 2016; Sun & Saenko, 2016). Baseline cross-crop transfer showed substantial generalization gaps (?25–35 percentage points), with accuracy ranging from 47.6% to 56.2% across crop pairs, confirming severe domain shift (Fu et al., 2021; Pan & Yang, 2010). Domain adaptation improved average cross-crop accuracy from 51.7% (baseline) to 58.3% (AdaBN), 60.1% (CORAL), 64.6% (MMD), and 73.2% (DANN), with DANN delivering up to ~19% absolute improvement and the most consistent gains under challenging transfers (Ganin et al., 2016). Overall, results indicate that adversarial domain adaptation can substantially reduce cross-crop failure modes and supports more scalable nitrogen monitoring with reduced dependence on crop-specific labels, while practical deployment should include agronomic guardrails and uncertainty-aware decision rules for safe in-season recommendations (Fu et al., 2021).