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The Contribution of Deep Learning Techniques in Research on Transition Process Towards More Sustainable and Resilient Agrifood Systems: Emerging Trends and Challenges

In: Artificial Intelligence and Networks for a Sustainable Future

Author

Listed:
  • Anna Rita Ceddia

    (University of Foggia, Department of Social Science)

  • Daniela Claps

    (ENEA—Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability)

  • Mariella Nocenzi

    (LUMSA (Libera Università degli Studi Maria SS. Assunta), Department of Human Sciences - Communication, Education and Psychology)

  • Maurizio Notarfonso

    (ENEA—Italian National Agency for New Technologies, Sustainable Agrifood Systems Division, Department for Sustainability)

  • Ombretta Presenti

    (ENEA—Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability)

Abstract

The agrifood system is a complex network that involves various activities, processes, and individuals working together to produce, process, distribute, and consume food (Braun et al. 2021). The agrifood system encompasses a wide range of actors, including seed, pesticide, fertilizer producers, livestock breeders, farmers, food processors, distributors, retailers, consumers, researchers, advocacy groups, and policymakers. The behaviours of these actors are influenced by a wide range of economic, environmental, social drivers, and their interactions (Ingram 2011).

Suggested Citation

  • Anna Rita Ceddia & Daniela Claps & Mariella Nocenzi & Maurizio Notarfonso & Ombretta Presenti, 2026. "The Contribution of Deep Learning Techniques in Research on Transition Process Towards More Sustainable and Resilient Agrifood Systems: Emerging Trends and Challenges," Contributions to Economics, in: Francesca Greco & Andrea Fronzetti Colladon & Peter A. Gloor (ed.), Artificial Intelligence and Networks for a Sustainable Future, pages 329-354, Springer.
    • Handle: RePEc:spr:conchp:978-3-032-13458-5_18
    DOI: 10.1007/978-3-032-13458-5_18
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