Design of appearance quality grading system for apricot mushroom based on machine vision

Abstract

Objective: This study aimed to induce the machine vision technology into the appearance detection of Pleurotus eryngii. Methods: A bilateral filter was proposed to replace Gaussian filter as image smoothing filter, and Ostu maximum inter-class variance method was proposed to replace the improved Canny operator, based on fixed double threshold segmentation, and used as edge detection algorithm. HALCON operator and color space conversion were used to extract the length, diameter, curvature, evenness, color and cap defects of P. erynii, and the development and design of visual software function modules were completed, under the VS 2017 development environment with HALCON 18.05 and C#. Results: 200 pieces of P. eryngii were randomly obtained to test the accuracy of the algorithm and the performance of the visual software. The diameter grading accuracy of the Pleurotus eryngii was 83%, and the remaining characteristic elements could reach more than 95%, with the overall classification accuracy of all specifications more than 90%. Conclusion: The classification of appearance quality of P. eryngii can be completed through the improvement of algorithm and the design of visual software.

Keywords

• apricot mushroom
• halcon
• grading and sorting
• image process