Assessing in-season crop nitrogen status based on UAV multispectral imaging and AI-driven model optimization

• Red-edge and NIR VIs correlated best with N indicators at DC55 and DC65 stages. • NSGA-based feature selection improved model stability and estimation accuracy. • KFNN models outperformed RF in estimating N uptake and DM, with D-index gains >10% • Top 3 VIs yielded robust models with D-index >0.75 across all indicators and stages. Maintaining an appropriate nitrogen (N) status is crucial for achieving desirable grain quality in barley. N fertilization is typically applied at sowing, leaving limited scope for in-season adjustment. Timely monitoring is essential for grain N concentration prediction and quality assessment. This study aimed to develop an artificial intelligence (AI) driven framework to estimate N concentration, N uptake, N nutrition index (NNI), and dry matter (DM) using unmanned aerial vehicle (UAV)-based multispectral imagery collected at three key growth stages (jointing, DC30; heading, DC55; and flowering, DC65). Two machine learning algorithms, random forest (RF) and Keras-based feedforward neural network (KFNN), were used to estimate N-related indicators. Multiple vegetation indices (VIs) were extracted from multispectral data. Feature importance ranking and non-dominated sorting genetic algorithm (NSGA) were applied to optimize input variables for improving model performance and robustness. The results showed that N-related indicators were better estimated at DC55 and DC65 stage, while DM were well predicted at DC30 stage. KFNN consistently outperformed RF in modeling complex traits such as N uptake and DM, with D-index improvements exceeding 10%. NSGA-optimized feature sets outperformed traditional importance ranking by improving both model stability and predictive accuracy. The top 3 VIs combinations achieved a favorable balance between accuracy and redundancy. All models achieved D-index values above 0.75 across N-related indicators and DM. This study demonstrates that combining growth-stage-specific modeling with AI-based feature optimization provides a scalable and cost-effective approach for real-time N monitoring, supporting precision N management in spring barley.