AbstractAn expanded description of particle morphology and the analysis of its relationships with physical properties may help to optimize the selection of raw materials and particle size fractions used as growing media constituents. Previous works have described the outlines of these relations based mostly on sieving procedures to characterize particle size distribution. They have shown limited and sometimes contradictory results due to the different methods used, size fractions selected, and physical properties measured. Also, sieve analysis, which separates particles based on their width, is less accurate for non‐spherical particles, which is the case for most growing media constituents. Recent works have promoted the use of dynamic image analysis (DIA) to precisely analyze both particle length and width. Five raw materials were chosen (white and black peats, coir, pine bark, and wood fiber) and sieved to obtain various particle size fractions. For each particle size fraction and the raw materials, the mean weight diameter (MWD), derived from sieving, was calculated, whereas mean particle length and width were determined using a DIA tool, the QicPic device. Also, physical properties were assessed from water retention curves established using Hyprop systems. The statement that the larger the particle size, the higher the air‐filled porosity (AFP), the lower the water holding capacity (WHC) was more precisely redefined. Large variations in WHC and AFP mainly occurred for finest particle size fractions, whereas changes were conversely very small or non‐existent for larger particle sizes. From data obtained for each particle size fractions, regression models were established to relate mean particle length and width (both determined using DIA) and MWD (determined from sieving) with WHC and AFP. Mean particle length was identified as the most relevant parameter for predicting WHC and AFP of the raw materials tested.