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Artificial Vision Technique to Detect and Classify Cocoa Beans

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Advances in Computational Intelligence (IWANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14135))

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Abstract

This article discusses the use of Artificial Intelligence (AI) to classify cocoa beans as healthy or diseased based on established classification criteria, given the challenges faced by the cocoa industry due to the impact of diseased beans on quality and grading. The proposed method uses YOLOv5 and achieved an 94.5% accuracy rate. The article also outlines the development of an affordable and easy-to-implement prototype system that cocoa farmers can use to grade and assure bean quality. The results suggest that the proposed system is successful, and increasing the amount of data improves its reliability, which could help farmers improve their competitiveness in the market.

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Author information

Authors and Affiliations

  1. School of Mathematical and Computational Sciences, Yachay Tech University, 100650, Urcuqui, Ecuador

    Luis Zhinin-Vera, Oscar Chang & Francesc Antón Castro

  2. School of Biological Sciences and Engineering, Yachay Tech University, 100119, Urcuqui, Ecuador

    Pamela Suárez

  3. Faculty of Agricultural Sciences, Universidad Técnica de Machala, 070151, Machala, Ecuador

    Jonathan Zhiminaicela-Cabrera

  4. MIND Research Group - Model Intelligent Networks Development, Urcuqui, Ecuador

    Luis Zhinin-Vera, Jonathan Zhiminaicela-Cabrera, Oscar Chang & Francesc Antón Castro

  5. LoUISE Research Group, I3A, University of Castilla-La Mancha, 02071, Albacete, Spain

    Luis Zhinin-Vera & Elena Pretel

  6. Instituto de Investigación en Informática de Albacete (I3A), Universidad de Castilla-La Mancha, Avenida de España s/n, 02071, Albacete, Spain

    Elena Pretel & Francisco López de la Rosa

  7. Department of Electrical, Electronic, Automatic and Communications Engineering, Universidad de Castilla-La Mancha, Avenida de España s/n, 02071, Albacete, Spain

    Francisco López de la Rosa

Authors
  1. Luis Zhinin-Vera
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  2. Jonathan Zhiminaicela-Cabrera
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  3. Elena Pretel
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  4. Pamela Suárez
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  5. Oscar Chang
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  6. Francesc Antón Castro
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  7. Francisco López de la Rosa
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Corresponding author

Correspondence to Luis Zhinin-Vera .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Málaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhinin-Vera, L. et al. (2023). Artificial Vision Technique to Detect and Classify Cocoa Beans. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_18

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  • DOI: https://doi.org/10.1007/978-3-031-43078-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43077-0

  • Online ISBN: 978-3-031-43078-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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