Non‐Destructive Quantification of Mycelial Biocomposite Growth Over Time

Mycelial biocomposites are sustainable alternatives to nonbiodegradable materials in building and packaging. Efficient manufacturing requires accurate, non‐destructive quantification of growth over time, yet existing methods are often destructive or imprecise. This study develops and evaluates several non‐destructive quantification methods for wood‐flour biocomposites by using images to define mycelial density levels, low (no visible growth, removable surface hyphal coverage < 7.8%/cm²), medium (light growth, 7.8%–26.7%/cm²), and high (dense coverage, > 26.7%/cm²), and tracking changes in each level over time. The first method, the manual creation of masks for each growth level, provided rapid but coarse classification, estimating 64.1% high growth after 16 days with 3.5% user variability. An algorithmic masking approach improved detail detection, increasing estimated high‐growth coverage to 81.7% but also variability to 9.3%. A fully automated deep‐learning model proved fastest and most consistent, yielding 77.8% high‐growth coverage and eliminating intra‐user variability (0%). The deep‐learning method was then applied to assess the effects of substrate supplements, revealing distinct growth patterns for each—differences not captured by traditional methods. These results demonstrate the effectiveness of automated quantification for mycelial biocomposites, enabling reproducible, high‐resolution, non‐destructive monitoring of growth and providing a foundation for more precise engineering and wider adoption of these materials.