Rapid Non-destructive Testing and Grading of Hericium erinaceus Based on Machine Vision
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Abstract:
The traditional quality inspection and classification of the edible mushroom Hericium erinaceus mainly depend on manual sorting, a process that is highly subjective and inefficient, resulting in uneven accuracy and significant waste of human and material resources. In order to realize the rapid non-destructive grade evaluation of H. erinaceus, we incorporated machine vision technology (image processing and software design) into the sorting and grading process. The color characteristics and grade of H. erinaceus were quickly detected by applying the additive color mixing model (RGB). Image threshold segmentation and Canny edge detection were used to determine the integrity of the material, and the minimum circumscribed circle method was used to calculate the size of the sample. A visual platform for rapid non-destructive testing of Hericium erinaceus quality was developed based on Microsoft Visual Studio 2017 platform. The results of these test confirmed the accuracy (97.07%) of the rapid non-destructive testing and grading system of Hericium erinaceus quality based on machine vision, and the process speed was more than five times that of the usual manual process. The reliability and feasibility of the system is verified, which should lead to further development of machine vision technology in the food processing industries.
