Particle size distribution of growing media constituents using dynamic image analysis: Parametrization and comparison to sieving

Abstract

Growing media constituents have heterogeneous particle size and shape, and their physical properties are partly related to them. Particle size distribution is usually analyzed through sieving process, segregating the particles by their width. However, sieving techniques are best describing more granular shapes and are not as reliable for materials exhibiting large varieties of shapes, like growing media constituents. A dynamic image analysis has been conducted for a multidimensional characterization of particle size distribution of several growing media constituents (white and black peats, pine bark, coir, wood fiber, and perlite), from particles that were segregated and dispersed in water. Diameters describing individual particle width and length were analyzed, then compared to particle size distribution obtained by dry and wet sieving methods. This work suggests the relevance of two parameters, Feret_MAX and Chord_MIN diameters for assessing particle length and width, respectively. They largely varied among the growing media constituents, confirming their non-spherical (i.e., elongated) shapes, demonstrating the advantages of using dynamic image analysis tools over traditional sieving methods. Furthermore, large differences in particle size distribution were also observed between dynamic image analysis and sieving procedures, with a finer distribution for dynamic image analysis. The discrepancies observed between methodologies were discussed (particle segregation, distribution weighing, etc.), while describing in details methodological limitations of dynamic image analysis.