摘要Annual climatic and agronomic shifts induce phenotypic plasticity,causing standard deep learning models to fail in high-throughput automated phenotyping tasks,such as alfalfa(Medicago sativa L.)seed maturity assessment.Here,we developed a deep learning-based transfer learning framework to confer climate robustness to such models,validated on a multispectral imaging dataset(365-970 nm)covering five maturity stages across three environmentally distinct years.We designed the Multispectral Spatial Attention Network(MSANet),a hybrid architecture integrating a 3D-CNN backbone with spectral and spatial attention modules to extract complex spatio-spectral features.On single-year data,MSANet achieved 93％classification accuracy,significantly sur-passing both traditional Support Vector Machine(77％)and deep learning baselines(e.g.,ResNet18,88％).However,this high intra-year performance did not generalize;direct model transfer to a different year caused accuracy to collapse to 41％,quantifying a profound domain shift.To mitigate this,We proposed an innovative Earth Mover's Distance(EMD)-guided'diagnose-adapt-finetune'framework.This approach utilized EMD to di-agnose layer-specific distributional shifts,employed EMD-guided Adaptive Batch Normalization(AdaBN)to align feature statistics across domains,and concluded with a data-efficient,few-shot fine-tuning strategy.The framework restored predictive accuracy to＞90％on out-of-domain data using only 100 labeled samples per class from the target year,representing an approximate 90％reduction in annotation costs compared to full super-vision.Crucially,the adapted model exhibited remarkable resilience to real-world data imperfections,main-taining stability under scenarios of class imbalance and label noise.Interpretability analyses further indicated that the model learned biologically plausible spectral correlates associated with seed maturation.Our work presents a generalizable methodology for developing environmentally robust phenotyping platforms,offering a promising pathway to enhance the reliability of AI systems in variable agricultural environments.