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Apple Spots and Defects Detection Based on Machine Vision, Fuzzy Systems, and Improved Gray Wolf Optimization Algorithm

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Metaheuristics and Optimization in Computer and Electrical Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1077))

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Abstract

One of the most important agricultural products that are produced in Iran and has a very high use is the apple-tree, which unfortunately sometimes suffers due to the existence of defects and surface defects. The quality control of apple trees before their export is of special importance as a strategic product in the country. Machine vision is one of the new methods in the automatic classification of apple trees, whose algorithms include three stages cutting the image of the apple from the background, extracting the image, and finally examining the presence of defects in the cut image of the apple. In the existing methods for apple-tree image segmentation, assuming that the background of the image is known in advance, apple-tree segmentation is easily done with the help of simple thresholding algorithms. Various methods in the field of image processing and machine vision have been presented for the quality control of this pure product based on image processing and machine vision, each of which has its advantages and disadvantages. Soft calculations were done to detect apple tree defects. The main goal of this chapter is to design a suitable system for detecting surface defects in apple trees. To analyze and check the accuracy of the proposed system, the above-mentioned parameters were applied to COFILAB database images with 100 images. The efficiency of the proposed method was evaluated by comparing it with two high-efficiency methods based on three measurement parameters. The results indicated that using the proposed method with an 87% correct detection rate compared to the SVM-PSO method with 80% and Features of Color with 84%, has improved the efficiency of the system.

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Author information

Authors and Affiliations

  1. School of Electrical and Computer, Engineering (FEEC), University of Campinas (Unicamp), Campinas-SP, Brazil

    Gabriel Gomes de Oliveira

  2. Department of Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Elham Moghadamnia

  3. Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Reza Radfar

  4. Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran

    Mohammad Worya Khordehbinan

  5. Avenida Libertador Bernardo O’Higgins 3363, University of Santiago of Chile (USACH), Santiago, Chile

    Mohammad Hosein Sabzalian

  6. Intelligent Medical Systems Department, Al-Mustaqbal University, 51001, Hillah, Babil, Iraq

    Maytham N. Meqdad

Authors
  1. Gabriel Gomes de Oliveira
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  2. Elham Moghadamnia
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  3. Reza Radfar
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  4. Mohammad Worya Khordehbinan
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  5. Mohammad Hosein Sabzalian
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  6. Maytham N. Meqdad
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Corresponding author

Correspondence to Mohammad Worya Khordehbinan .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), Ankara, Türkiye

    Navid Razmjooy

  2. Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), Ankara, Türkiye

    Noradin Ghadimi

  3. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    Venkatesan Rajinikanth

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de Oliveira, G.G., Moghadamnia, E., Radfar, R., Khordehbinan, M.W., Sabzalian, M.H., Meqdad, M.N. (2023). Apple Spots and Defects Detection Based on Machine Vision, Fuzzy Systems, and Improved Gray Wolf Optimization Algorithm. In: Razmjooy, N., Ghadimi, N., Rajinikanth, V. (eds) Metaheuristics and Optimization in Computer and Electrical Engineering. Lecture Notes in Electrical Engineering, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-42685-8_4

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