Recognition of growth characteristics during recovery of seafood mushroom based on machine vision

In the actual mushroom house environment, the interference from background and blue light has a significant impact on the identification effect of seafood mushroom mycelium in the recovery period. Therefore, an intelligent computation method based on improved threshold processing using machine vision was proposed. To address the above problems, image noise was first removed by Gaussian smoothing, and then the background-free mycelium image was extracted by top-hat adaptive binarization method (TH-ATB) combined with maximum area contour detection. Subsequently, the improved automatic white balance algorithm (IAWB-GWPR) is integrated with contrast limited adaptive histogram equalization (CLAHE) to address blue light interference. Finally, the gray histogram is used to calculate mycelium density and coverage, while two-dimensional Gaussian fitting assesses distribution uniformity. Experimental results demonstrate that the proposed algorithm achieves a coefficient of determination of 0.94 under blue light conditions, improving by 0.15, 0.14, and 0.35 compared to the no treatment, IAWB-GWPR, and CLAHE methods, respectively. The interval ratios for density and coverage are 83.64% and 88.98%, with relative deviations remaining within acceptable limits.