Author(s)

Zhijin Wei ¹

Affiliation(s)

¹ College of Information and Electrical Engineering, China Agricultural University, Beijing, China

Corresponding Author

Zhijin Wei

ABSTRACT

This paper addresses the issues of low efficiency and insufficient classification accuracy in the processing and classification of shiitake mushrooms by proposing an automatic sorting model based on image processing. First, background removal of mushroom images is performed using an open-source library, and color images are converted to grayscale using the weighted average method to reduce computational complexity and noise interference. Next, Gaussian filtering is applied to smooth the images while preserving important structural information. Subsequently, the Canny operator is utilized for edge detection to extract the contours of the mushrooms. Using OpenCV's findContours function with a set minimum area threshold, effective mushroom contours are filtered out, achieving rapid and accurate localization. In the classification stage, standard deviation and maximum connected region ratio are selected as the primary features, and a linear classifier is established using Support Vector Machine (SVM) to differentiate between Type A and Type B mushrooms. Experimental results show that the proposed model achieves a classification accuracy of 85% and an F1 score of 0.83, demonstrating its effectiveness in enhancing sorting efficiency and classification precision.

KEYWORDS

Shiitake classification, image processing, edge detection, support vector machine, automatic sorting

CITE THIS PAPER

Zhijin Wei, Automatic Sorting Model for Shiitake Mushrooms Based on Image Processing and Support Vector Machine. Journal of Image Processing Theory and Applications (2024) Vol. 7: 152-157. DOI: http://dx.doi.org/10.23977/jipta.2024.070118.

REFERENCES

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[2] Deng J, Liu Y, Xiao X. Deep-Learning-Based Wireless Visual Sensor System for Shiitake Mushroom Sorting[J]. Sensors, 2022, 22(12): 4606.
[3] Wang F, Zheng J, Tian X, et al. An automatic sorting system for fresh white button mushrooms based on image processing [J]. Computers and electronics in agriculture, 2018, 151: 416-425.
[4] Maurya P, Singh N P. Mushroom classification using feature-based machine learning approach[C]//Proceedings of 3rd International Conference on Computer Vision and Image Processing: CVIP 2018, Volume 1. Springer Singapore, 2020: 197-206. 
[5] Yin H, Yi W, Hu D. Computer vision and machine learning applied in the mushroom industry: A critical review[J]. Computers and Electronics in Agriculture, 2022, 198: 107015.
[6] Bouganssa T, Salbi A, Aarabi S, et al. Recognition of Mushrooms and Classification of Edible and Toxic Families using Hardware Implementation of CNN Algorithms on an Embedded system[J]. Research Journal of Pharmacy and Technology, 2024, 17(2): 860-866.

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