Precision nitrogen (N) management requires rapid and real-time technologies for in-season crop N status diagnosis. The leaf fluorescence sensor Dualex 4 is an effective and promising tool to monitor crop N status. N nutrition index (NNI) is the most widely recognized diagnostic tool for accurate in-season diagnosis of crop N status. However, studies focusing on revealing the relationships between fluorescence sensing indices and NNI and assessing the N status of maize is limited. The objectives of this study were to (1) evaluate the potential of using Dualex 4 indices measured on three differently positioned leaves to estimate NNI across different stages; and (2) determine if the incorporation of environmental (weather) and management information can significantly improve the in-season N status prediction and diagnosis of maize. In 2016 and 2017, a total of four experiments with six N rates and three plant densities were conducted in two fields in Northeast China. Dualex sensor readings – Chlorophyll (Chl) and N balance index (NBI) – were collected from three differently positioned leaves at three growth stages. Some external factors including weather and management conditions were included for in-season N status assessment. The results indicated that the two Dualex indices (Chl and NBI) had strong relationships with NNI at different growth stages, and both stage-specific and across-stage models could estimate NNI based on their values acquired from differently positioned leaves. Nevertheless, the N diagnostic accuracies based on the estimated NNI by the Dualex indices were not satisfactory with Kappa values all lower than 0.40. Likewise, similar results were found in the multiple linear regression (MLR) models only based on the Dualex readings (MLR_Chl, MLR_NBI and MLR_Chl+NBI). However, when weather and management variables were used together with Dualex sensor measurements in MLR analysis, the prediction of NNI (R² = 0.81 to 0.85) and the accuracy of maize N status diagnosis (areal agreement = 0.79 and Kappa = 0.52 to 0.55) were significantly improved. More studies are needed to develop strategies combining more environmental and management variables with sensor data to further improve in-season N status diagnosis and N management and/or combine proximal with remote sensing for large-scale crop N nutritional status diagnosis and in-season site-specific N management.