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Pre-processing Techniques and Model Aggregation for Plant Disease Prevention

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Highlights in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection (PAAMS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1838))

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Abstract

Image processing techniques have become increasingly popular in plant disease classification. However, one of the major challenges in this field is accurately identifying and classifying different diseases based on plant images. Pre-processing techniques such as smoothing and sharpening can play a crucial role in improving the accuracy of disease classification. These techniques can enhance the quality of the images and reduce noise, making it easier for machine learning algorithms to extract meaningful features from the images. In this context, these techniques can significantly improve the overall accuracy of plant disease classification systems. This paper aims to explore the potential of preprocessing techniques such as smoothing and sharpening in enhancing the quality of plant images for disease classification.

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Acknowledgements

This work was partially supported with grant PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, Consellería d’Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future) and grant from the Research Services of Universitat Politècnica de València (PAID-PD-22).

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Authors and Affiliations

  1. Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022, Valencia, Spain

    C. Marco-Detchart, J. A. Rincon, V. Julian & C. Carrascosa

  2. Valencian Graduate School and Research Network of Artificial Intelligence (VALGRAI), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022, Valencia, Spain

    J. A. Rincon & V. Julian

Authors
  1. C. Marco-Detchart
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  2. J. A. Rincon
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  3. V. Julian
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  4. C. Carrascosa
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Corresponding author

Correspondence to C. Marco-Detchart .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Dalila Durães

  2. University of Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. University Rey Juan Carlos, Mostoles, Spain

    Marin Lujak

  4. German International University, Cairo, Egypt

    Alia El Bolock

  5. ISEP/GECAD, Porto, Portugal

    João Carneiro

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Marco-Detchart, C., Rincon, J.A., Julian, V., Carrascosa, C. (2023). Pre-processing Techniques and Model Aggregation for Plant Disease Prevention. In: Durães, D., González-Briones, A., Lujak, M., El Bolock, A., Carneiro, J. (eds) Highlights in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Communications in Computer and Information Science, vol 1838. Springer, Cham. https://doi.org/10.1007/978-3-031-37593-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-37593-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37592-7

  • Online ISBN: 978-3-031-37593-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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