Accurately monitoring and assessing crop growth dynamics are important for precision crop management. Numerous studies have been performed to describe the relationships between crop growth traits and environmental conditions. The objective of this study was to develop a statistical model for the comprehensive assessment of wheat plant growth dynamics by investigating the correlations between the main environmental factors and plant growth morphological characteristic indicators. The environmental factors included the accumulated air temperature (AAT), accumulated soil temperature (AST), accumulated precipitation (AP), accumulated sunshine duration (ASD), accumulated solar radiation (ASR) and plant density (PD). The plant growth characteristic indicators included the main stem leaf age (MSLA), tiller number per plant (TNPP), secondary root number (SRN), population stem and tiller number (PSTN) and leaf area index (LAI). The environmental factor data were automatically acquired in the field. At the same time, the wheat plant growth characteristic data were measured from the field-grown wheat sown on seven dates at six plant density treatments. A partial least squares regression (PLSR)-based algorithm was designed to analyze and establish the statistical model between the environmental factors and wheat plant growth characteristic indicators. The results indicated that each of the five wheat plant growth characteristic indicators can be modeled accurately (coefficient of determination (R²) > 70% and ratio of prediction to deviation (RPD) > 1.9) in the calibration and validation of the proposed model. The model can be considered a practical and accurate tool used for the real-time ongoing assessment of wheat plant growth dynamics. Future research is needed to test the model more thoroughly for higher accuracy and wider use by designing experiments in different ecoregions and varieties.