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Rapid Detection of Changes in Image Textures of Carrots Caused by Freeze-Drying using Image Processing Techniques and Machine Learning Algorithms

Author

Listed:
  • Ewa Ropelewska

    (Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

  • Kadir Sabanci

    (Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey)

  • Muhammet Fatih Aslan

    (Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey)

  • Necati Çetin

    (Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey)

Abstract

The objective of this study was to evaluate the differences in texture parameters between freeze-dried and fresh carrot slices using image processing and artificial intelligence. Images of fresh and freeze-dried carrot slices were acquired using a digital camera. Texture parameters were extracted from slice images converted to individual color channels L , a , b , R , G , B , X , Y , and Z . A total of 1629 texture parameters, 181 for each of these color channels, were obtained. Models for the classification of freeze-dried and fresh carrot slices were created using various machine learning algorithms, based on attributes selected from a combined set of textures extracted from images in all color channels ( L , a , b , R , G , B , X , Y , and Z ). Using three different feature selection methods (Genetic Search, Ranker, and Best First), the 20 most effective texture parameters were determined for each method. The models with the highest classification accuracy obtained by applying various machine learning algorithms from Trees, Rules, Meta, Lazy, and Functions groups were determined. The classification successes obtained with the parameters selected from all three different feature selection algorithms were compared. Random Forest, Multi-class Classifier, Logistic and SMO machine learning algorithms achieved 100% accuracy in the classification performed with texture features obtained by each feature selection algorithm.

Suggested Citation

  • Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan & Necati Çetin, 2023. "Rapid Detection of Changes in Image Textures of Carrots Caused by Freeze-Drying using Image Processing Techniques and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(8), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:7011-:d:1129644
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    References listed on IDEAS

    as
    1. Ewa Ropelewska & Xiang Cai & Zhan Zhang & Kadir Sabanci & Muhammet Fatih Aslan, 2022. "Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum ( Prunus domestica L.) Kernels," Agriculture, MDPI, vol. 12(2), pages 1-12, February.
    2. Kadir Sabanci & Muhammet Fatih Aslan & Vanya Slavova & Stefka Genova, 2022. "The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line," Agriculture, MDPI, vol. 12(10), pages 1-11, October.
    3. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdoulghafor & Samir Brahim Belhaouari & Normahira Mamat & Shamsul Faisal Mohd Hussein, 2022. "Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review," Agriculture, MDPI, vol. 12(7), pages 1-35, July.
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