An Algorithm to Extract the Defective Areas of Potato Tubers Infected with Black Scab Disease Using Fuzzy C Means Clustering for Automatic Grading

Document Type : Original Research

Authors

1 Faculty of Engineering, Islamic Azad University, Bostanabad Branch, East Azerbaijan, Iran.

2 Agricultural Machinery Engineering Department, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

3 Department of Biotechnology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran.

4 Biosystems Engineering Department, Agriculture Faculty, Shiraz University, Shiraz, Iran.

Abstract

Estimating the surface area of defects of diseased potatoes is a key factor in the automatic grading of this product. In this article, an algorithm has been developed using fuzzy clustering method and image processing functions to estimate the defective areas of potato tubers infected with black scab disease. Fuzzy clustering, which is an unsupervised method, was used to segment color images and extract defective areas of potatoes, and image processing functions have been used to extract the total area of potatoes. In the segmentation method based on fuzzy clustering, the data matrix related to potato images were divided into separate clusters in a fuzzy way, in which the boundaries of the clusters are defined in a fuzzy way instead of being definite and specific. The results showed that this algorithm is very efficient for extracting black scab disease and can be used to extract the amount of diseases that can be used for automatic grading of this product based on the American standards.

Keywords

References

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Biomechanism and Bioenergy Research
Volume 2, Issue 1
June 2023
Pages 32-39

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History

  • Receive Date: 26 June 2023
  • Revise Date: 27 June 2023
  • Accept Date: 28 June 2023

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