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Integrating Predictive Process Monitoring Techniques in Smart Agriculture

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Foundations of Intelligent Systems (ISMIS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14670))

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Abstract

Problems related to the environment are increasingly commonly known and consequently also technology is adapting to find suitable solutions. The ancestral technique of crop rotation was identified as a solution to address the problems related to pollution due to intensive food production (i.e. using fertilizers and pesticides). To ensure that this technique can actually improve food production, it is necessary to understand how modern technologies can support it; in particular the analysis of crop rotation can support farmers in decision making process and the optimization of farm management practices. The aim of this paper is to investigate how predictive process monitoring techniques can enhance crop rotation strategies by leveraging Agriculture 4.0 through real-time monitoring, resulting in more accurate and adaptive strategies. It is a position paper that proposes research questions for further study, which may help to develop the research area.

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Author information

Authors and Affiliations

  1. CIRAD, INRIA, Univ. Montpellier, Montpellier, France

    Roberto Interdonato

  2. INRAE, INRIA, Univ. Montpellier, Montpellier, France

    Dino Ienco

  3. University of Naples Federico II, 08544, Naples, Italy

    Simona Fioretto & Elio Masciari

Authors
  1. Simona Fioretto
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  2. Dino Ienco
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  3. Roberto Interdonato
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  4. Elio Masciari
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Corresponding author

Correspondence to Simona Fioretto .

Editor information

Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. University of Sorbonne Paris Nord, Villetaneuse, France

    Hanane Azzag

  3. Claude Bernard University Lyon 1, Villeurbanne Cedex, France

    Mohand-Said Hacid

  4. LIAS/ENSMA, Poitiers, Cedex, France

    Allel Hadjali

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew Ras

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Fioretto, S., Ienco, D., Interdonato, R., Masciari, E. (2024). Integrating Predictive Process Monitoring Techniques in Smart Agriculture. In: Appice, A., Azzag, H., Hacid, MS., Hadjali, A., Ras, Z. (eds) Foundations of Intelligent Systems. ISMIS 2024. Lecture Notes in Computer Science(), vol 14670. Springer, Cham. https://doi.org/10.1007/978-3-031-62700-2_27

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  • DOI: https://doi.org/10.1007/978-3-031-62700-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-62699-9

  • Online ISBN: 978-3-031-62700-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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