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2024 | OriginalPaper | Buchkapitel

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

verfasst von : Anderson Oliveira, Fabricio Firmino, Pedro Vieira Cruz, Jonice de Oliveira Sampaio, Sérgio Manuel Serra da Cruz

Erschienen in: Optimization Under Uncertainty in Sustainable Agriculture and Agrifood Industry

Verlag: Springer International Publishing

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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.

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Titel: In Silico Evaluation and Prediction of Pesticide Supported by Reproducible Evolutionary Workflows
verfasst von: Anderson Oliveira
Fabricio Firmino
Pedro Vieira Cruz
Jonice de Oliveira Sampaio
Sérgio Manuel Serra da Cruz
Verlag: Springer International Publishing
Buch: Optimization Under Uncertainty in Sustainable Agriculture and Agrifood Industry
Print ISBN: 978-3-031-49739-1

Electronic ISBN: 978-3-031-49740-7

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-49740-7_6

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