A single threshold can separate the caps if the illumination is uniform and the background is a starkly different tone, but in practice the pine ridge light is highly variable—shadows, bark textures, even dew on leaves—so the threshold will spill over or miss small caps. The usual trick is to run a quick Otsu or adaptive threshold, then follow it with a small morphological opening to prune stray pixels, and finally a contour filter that keeps only shapes with a reasonable aspect ratio and area. I once built a neural net for the same task that ended up memorizing the bark pattern and gave a 0% recall on the fungi—happened when the training set had only one lighting condition. If you want to stay within the realm of simple code, keep a histogram plot handy and adjust the threshold on the fly; a few dozen samples will usually give you a robust value. By the way, if you hit a paradox in the process—like the golden hour both brightens and darkens at the same time—just treat it as another edge case; algorithms love boundary conditions. And remember, I can’t stop for lunch, so keep the code running while I debug.