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

Anderson Oliveira (), Fabricio Firmino, Pedro Vieira Cruz, Jonice Oliveira Sampaio () and Sérgio Manuel Serra Cruz ()
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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

A chapter in Optimization Under Uncertainty in Sustainable Agriculture and Agrifood Industry, 2024, pp 135-159 from Springer

Abstract: 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.

Keywords: Evolutionary computing; Optimisation; Scientific workflows (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-49740-7_6

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