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In Silico Evaluation and Prediction of Pesticide Supported by Reproducible Evolutionary Workflows

In: Optimization Under Uncertainty in Sustainable Agriculture and Agrifood Industry

Author

Listed:
  • Anderson Oliveira

    (Federal Rural University of Rio de Janeiro)

  • Fabricio Firmino

    (Federal University of Rio de Janeiro)

  • Pedro Vieira Cruz

    (Federal Rural University of Rio de Janeiro)

  • Jonice Oliveira Sampaio

    (Federal University of Rio de Janeiro)

  • Sérgio Manuel Serra Cruz

    (Federal Rural University of Rio de Janeiro
    Federal University of Rio de Janeiro)

Abstract

Agriculture plays an essential role in sustaining human activities. Challenges such as the indiscriminate use of pesticides pose a threat to food security. Evolutionary computing (EC) has emerged as a robust computational methodology for the treatment of many complex agricultural problems in recent years. In addition, scientific workflows are a technology that supports the automation and reproducibility of large-scale in silico experiments. However, the design of evolutionary workflows is still an open issue for decision-makers. Therefore, to bridge this gap, we present a novel approach to help researchers model evolutionary workflows. To answer this question, in this chapter, we use VisPyGMO, which offers a set of evolutionary algorithm modules that help researchers build reusable evolutionary workflows more efficiently. Moreover, we show the feasibility of VisPyGMO in analysing a large real-world agricultural dataset used to respond to competency questions (CQ) and predict future use of pesticides.

Suggested Citation

  • Anderson Oliveira & Fabricio Firmino & Pedro Vieira Cruz & Jonice Oliveira Sampaio & Sérgio Manuel Serra Cruz, 2024. "In Silico Evaluation and Prediction of Pesticide Supported by Reproducible Evolutionary Workflows," Springer Books, in: Víctor M. Albornoz & Alejandro Mac Cawley & Lluis M. Plà-Aragonés (ed.), Optimization Under Uncertainty in Sustainable Agriculture and Agrifood Industry, pages 135-159, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-49740-7_6
    DOI: 10.1007/978-3-031-49740-7_6
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