The most commonly reported characteristics of hemp shives used in hemp–lime composites are bulk density and origin (see Table 2). Bulk density provides a general indication of particle size and shape, while the material’s origin may reflect the processing technology, and thus, the quality of the aggregate (e.g., fiber and dust content and granulometric uniformity) [27]. Nevertheless, details regarding the presence of fibers and fines—which can significantly affect composite properties—are often omitted or limited to vague statements.

Another important property that is sometimes reported or investigated is the water absorption capacity of hemp shives. This parameter influences the amount of water required in the mix to ensure proper binder hydration, as well as the drying time of the material and the potential risk of biological degradation [28].

Among the most frequently provided parameters by hemp shive suppliers is the particle size distribution. According to French hemp construction association standards, this distribution defines the size range within which 95% of the particles fall [29]. However, this range is often very broad and does not provide a precise representation of the actual particle dimensions. Furthermore, hemp shives exhibit an elongated shape, meaning that reporting only a single dimension can be misleading. Occasionally, granulometry is described using a generalized dimension (e.g., 5 × 5 × 15 mm) [30], a broad range of geometric attributes, or a vague reference to particle length [31].

To precisely describe the shape of hemp shives, researchers most commonly report a set of dimensional parameters: the average particle length and width, the major and minor axes of an ellipse fitted to the aggregate particle, or the maximum and minimum Feret diameters (see Figure 4).

There is no clearly defined standard for determining the length and width of irregularly shaped particles such as hemp shives. These dimensions are most commonly obtained through image analysis techniques [16,35,36,41]. Many researchers manually measure a batch of several grams of particles using tools such as calipers, which is a highly time-consuming task due to the small size of the aggregate [39]. Based on these measurements, parameters such as particle elongation—defined as the ratio of the major to minor axis [7,16]—or the width-to-length ratio [4,33] can be calculated.

Image analysis also enables accurate measurement of the surface area of each individual particle. From these data, researchers often derive the equivalent diameter, defined as the diameter of a circle having the same area as the measured particle [16] (see Figure 4).

Another useful parameter for describing particle shape is circularity, where a value of 0 corresponds to an infinitely elongated shape and a value of 1 represents a perfect sphere [16]. Circularity is calculated using the following Equation (1): where A is the particle surface area and P is the perimeter.

Reporting only the average size of hemp shives is often insufficient due to the high variability in particle surface areas. Therefore, researchers frequently use graphical methods to illustrate the distribution of particle characteristics, such as elongation, circularity, or size (e.g., length, width, or axes) [4]. Rather than referencing particle count, these analyses typically rely on the cumulative area represented by the particles [41]. This approach provides a clearer representation of how specific sizes or shapes are distributed across the total volume (or surface area) of the aggregate.

In recent years, increased focus on sustainable construction has driven research into composite materials derived from renewable resources. The RILEM Technical Committee TC 236-BBM (2010–2016) developed recommendations for characterizing bio-aggregates, with a particular emphasis on hemp shives. The committee highlighted that traditional sieve analysis is less suitable for these materials due to their irregular, elongated, and porous particle structure, which can lead to inaccurate granulometric classification. Instead, it recommends advanced imaging and morphological analysis, such as digital image-based techniques, for precise determination of particle dimensions and shapes. While sieve analysis may be used as a comparative method with clearly defined testing conditions, the committee advocates adopting modern measurement techniques to enhance result repeatability and reliability [42].

The various available methods for measuring the geometric characteristics of hemp shives differ significantly in terms of data resolution, accuracy, and experimental requirements (see Table 3). Sieve analysis enables the examination of relatively large sample masses—for example, 100 g of hemp shives were sieved in [34], and up to 112 g in [39]. Such quantities contain tens of thousands of particles, providing a statistically stable and representative granulometric distribution. However, the main source of uncertainty in this method is that irregular particles can pass through sieve openings based on their orientation, not actual dimensions. Elongated fragments may be classified into smaller fractions if they pass vertically through the mesh despite their longer dimension. Consequently, sieve analysis primarily reflects particle width rather than true length or shape [32,34,39]. Furthermore, intensive sieving may cause fragile particles to break apart, increasing the proportion of fine fractions (dust).

A non-invasive method of size determination is manual measurement using calipers. This method is inherently time-consuming and typically includes hundreds of individual particles. For example, in study [9], over 1000 fragments per sample were manually measured for length and width. Such high numbers are rare; more often, researchers limit the sample to a few hundred or fewer representatively selected particles, which may compromise the accuracy of the size distribution. Additionally, manual caliper measurements are subject to human error. Consistently applying the caliper to each irregular particle is challenging, and selecting what constitutes “length” or “width” may be subjective (e.g., whether to include protruding fibers). Although digital calipers typically have high resolution (0.01 mm), human error and fatigue over time can result in deviations of several tenths of a millimeter.

Two-dimensional (2D) image analysis methods—based on digital processing of scanned or photographed particles—allow for the automated determination of length, width, projected area, and shape parameters for each individual particle. These methods offer a more representative depiction of particle geometry than traditional sieving. Studies have confirmed that granulometric curves obtained via sieve analysis and 2D image analysis differ substantially [25]. Relying solely on sieve methods for plant-based aggregates can result in imprecise observations, whereas image analysis provides a more accurate description of particle morphology [38].

Software such as ImageJ enables automatic measurement of hundreds or thousands of particles from relatively small samples (typically 3–20 g) [4,33,38]. Due to the low density of hemp shives, even small sample masses translate into large particle counts. According to RILEM TC 236-BBM guidelines, image analysis should use 3–6 g of shives, which corresponds to at least 2000 particles—a quantity deemed sufficient to achieve a statistically representative size distribution [42]. Increasing the number of particles enhances statistical reliability but also increases the workload of data preparation and processing.

Proper image preparation and processing are crucial for 2D image analysis. Overlapping or touching particles may be interpreted as a single large particle, inflating the measured dimensions. The RILEM protocol recommends placing hemp shives flat and separated on the scanner surface. Another major source of error is the thresholding (binarization) level used to segment particles. If the threshold is too low, thin particle edges may be lost; if it is too high, adjacent particles may merge or appear oversized due to halo effects. Careful selection of the threshold level is therefore critical [33]. Dust and very fine particles may introduce noise, distorting the distribution by contributing a large number of small objects.

One limitation of 2D image analysis is that it does not capture particle thickness (the third dimension). Estimating volume or 3D shape requires additional assumptions or independent thickness measurements. For example, in [4], particle volume was approximated by assuming that thickness is proportional to particle width. While this assumption allows rough volume estimation, it introduces uncertainty and simplifies the real particle geometry. More accurate 3D characterization of hemp shives can be obtained via X-ray computed tomography (CT) [10]. This method enables 3D reconstruction of particle morphology, providing precise measurements of size, volume, porosity, and shape factors. Despite its accuracy, CT is limited by high equipment costs, the need for specialized personnel, long scan times at high resolutions, sample preparation constraints, and the computational demands of 3D data analysis. As such, it remains impractical for routine use, especially in studies with large sample sizes or limited resources.

Therefore, the aim of this study is to assess the influence of hemp shive granulometry on the thermal conductivity of hemp–lime composites under controlled conditions, using image-based aggregate characterization.