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Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning Techniques

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Optimization, Learning Algorithms and Applications (OL2A 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2280))

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Abstract

Olive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield and quality. This study addresses the challenge of improving the diagnosis of diseases in olive trees, specifically focusing on aculus olearius and Olive Peacock Spot diseases. Using a novel hybrid approach that combines deep learning and machine learning methodologies, the authors aimed to optimize disease classification accuracy by analyzing images of olive leaves. The presented methodology integrates Local Binary Patterns (LBP) and an adapted ResNet50 model for feature extraction, followed by classification through optimized machine learning models, including Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrated that the hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming existing models. This advancement underscores the potential of integrated technological approaches in agricultural disease management and sets a new benchmark for the early and accurate detection of foliar diseases.

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Notes

  1. 1.

    https://scikit-learn.org/stable.

  2. 2.

    https://www.tensorflow.org/.

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Acknowledgements

The cooperation was supported by the HORIZON-WIDERA-2021-ACCESS-03-01 STEP - STEM Research and Equality, Diversity and Inclusion Project, under Grant Agreement No. 101078933. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), ALGORITMI (UIDB/00319/2020) and SusTEC (LA /P/0007/2021).

Author information

Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    João Mendes, Guido S. Berger, José Lima & Ana I. Pereira

  2. ALGORITMI Center, University of Minho, 4710-057, Braga, Portugal

    João Mendes & Lino Costa

  3. Lab-I*, Université de Reims Champagne-Ardenne, 51100, Reims, France

    Juliet Moso & Zahia Guessoum

Authors
  1. João Mendes
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  2. Juliet Moso
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  3. Guido S. Berger
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  4. José Lima
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  5. Lino Costa
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  6. Zahia Guessoum
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  7. Ana I. Pereira
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Corresponding author

Correspondence to João Mendes .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  3. Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  5. Department of Electrical Engineering, Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  7. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

  8. Dept ISAATC, Universidad de La Laguna, San Cristóbal de La Laguna, Spain

    Santiago T. Álvarez

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Mendes, J. et al. (2024). Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning Techniques. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2280. Springer, Cham. https://doi.org/10.1007/978-3-031-77426-3_11

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