Plant density is an important variable for management and phenotyping of small-grain cereal crops such as wheat and barley. While many image-based estimation methods exist to replace laborious manual counting, most of them rely on empirical relationships that may not generalize well to different sites, growth stages, species and varieties. In this study, we propose a novel small-grain cereal plant density estimation method that uses leaf tip density dynamics derived from submillimeter-scale images acquired at 45° view zenith angle. This method contained two steps. In the first step, a P2PNet deep learning detection model was trained to estimate leaf tip count in a surface of known area to get the leaf tip density. An occlusion correction method was then applied on this density, leading to an estimation error of about 20% at critical growth stages. In the second step, a wheat leaf dynamic model was used to simulate the evolution of leaf tip density over thermal time as functions of several variables, including mean time of plant emergence, phyllochron and plant density. This model was then inverted using a lookup table approach to estimate plant density from leaf tip density dynamics. The results obtained on three test datasets indicated that two observations performed before the appearance of the second and third leaves could be sufficient to attain a relative plant density estimation error of about 10%. We also discussed that this method should be able to work on other datasets without recalibration, and estimate other variables such as phyllochron at early growth stages. The code will be available at: https://github.com/wdwzytc/WheatPlantDensity.