IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v8y2023i6p99-d1159829.html
   My bibliography  Save this article

Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces

Author

Listed:
  • Kacoutchy Jean Ayikpa

    (Laboratoire Imagerie et Vision Artificielle (ImVia), Université Bourgogne Franche-Comté, 21000 Dijon, France
    Laboratoire de Mécanique et Information (LaMI), Université Felix Houphouët-Boigny, Abidjan 22 BP 801, Côte d’Ivoire
    Unité de Recherche et d’Expertise Numérique (UREN), Université Virtuelle de Côte d’Ivoire, Abidjan 28 BP 536, Côte d’Ivoire)

  • Diarra Mamadou

    (Laboratoire de Mécanique et Information (LaMI), Université Felix Houphouët-Boigny, Abidjan 22 BP 801, Côte d’Ivoire)

  • Pierre Gouton

    (Laboratoire Imagerie et Vision Artificielle (ImVia), Université Bourgogne Franche-Comté, 21000 Dijon, France)

  • Kablan Jérôme Adou

    (Laboratoire de Mécanique et Information (LaMI), Université Felix Houphouët-Boigny, Abidjan 22 BP 801, Côte d’Ivoire)

Abstract

Côte d’Ivoire, the world’s largest cocoa producer, faces the challenge of quality production. Immature or overripe pods cannot produce quality cocoa beans, resulting in losses and an unprofitable harvest. To help farmer cooperatives determine the maturity of cocoa pods in time, our study evaluates the use of automation tools based on similarity measures. Although standard techniques, such as visual inspection and weighing, are commonly used to identify the maturity of cocoa pods, the use of automation tools based on similarity measures can improve the efficiency and accuracy of this process. We set up a database of cocoa pod images and used two feature extractors: one based on convolutional neural networks (CNN), in particular, MobileNet, and the other based on texture analysis using a gray-level co-occurrence matrix (GLCM). We evaluated the impact of different color spaces and feature extraction methods on our database. We used mathematical similarity measurement tools, such as the Euclidean distance, correlation distance, and chi-square distance, to classify cocoa pod images. Our experiments showed that the chi-square distance measurement offered the best accuracy, with a score of 99.61%, when we used GLCM as a feature extractor and the Lab color space. Using automation tools based on similarity measures can improve the efficiency and accuracy of cocoa pod maturity determination. The results of our experiments prove that the chi-square distance is the most appropriate measure of similarity for this task.

Suggested Citation

  • Kacoutchy Jean Ayikpa & Diarra Mamadou & Pierre Gouton & Kablan Jérôme Adou, 2023. "Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces," Data, MDPI, vol. 8(6), pages 1-24, May.
  • Handle: RePEc:gam:jdataj:v:8:y:2023:i:6:p:99-:d:1159829
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/8/6/99/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/8/6/99/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dominic Edelmann & Konstantinos Fokianos & Maria Pitsillou, 2019. "An Updated Literature Review of Distance Correlation and Its Applications to Time Series," International Statistical Review, International Statistical Institute, vol. 87(2), pages 237-262, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominic Edelmann & Tobias Terzer & Donald Richards, 2021. "A Basic Treatment of the Distance Covariance," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 12-25, May.
    2. Emmanuel Selorm Tsyawo, 2023. "Feasible IV regression without excluded instruments," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 235-256.
    3. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2020. "Change-point methods for multivariate time-series: paired vectorial observations," Statistical Papers, Springer, vol. 61(4), pages 1351-1383, August.
    4. Marc Hallin & Simos Meintanis & Klaus Nordhausen, 2024. "Consistent Distribution–Free Affine–Invariant Tests for the Validity of Independent Component Models," Working Papers ECARES 2024-04, ULB -- Universite Libre de Bruxelles.
    5. Gizem Hayrullahoğlu & Çiğdem Varol, 2022. "Understanding mobility dynamics using urban functions during the COVID-19 pandemic: comparison of pre-and post-new normal eras," Asia-Pacific Journal of Regional Science, Springer, vol. 6(3), pages 1087-1109, October.
    6. Dominic Edelmann & Thomas Welchowski & Axel Benner, 2022. "A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing," Biometrics, The International Biometric Society, vol. 78(3), pages 867-879, September.
    7. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:8:y:2023:i:6:p:99-:d:1159829. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.