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An Entropic Approach to Assess People’s Awareness of the Health Risks Posed by Pesticides in Oenotourism Events

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1366)

Abstract

Wine production and vineyard work are seasonal activities as there is a period when no intervention is required and oenotourism may fill that void. On the other hand, given that grapevines are tied to this area of activity, it is of paramount importance to assess customer and staff awareness of the health risks of pesticide use, that emerge from people's responses to specific questionnaires. Therefore, a workable problem-solving method is proposed which enables one to assess the level of awareness of people who are taking on the risks, evaluated in terms of an estimation of the individuals entropic state with respect to this particular issue. The analysis and development of such a model is based on a number of Logic Programming formalisms for Knowledge Representation and Reasoning, tha are consistent with an Artificial Neural Network approach to computing. The data collection process involved 173 participants. The proposed system presents an accuracy of about 90% and enables the diagnosis of risk awareness and the correspondent fragilities among customers and staff for a particular pesticide.

Keywords

  • Oenotourism
  • Health risks
  • Pesticides
  • Entropy
  • Logic programming
  • Knowledge representation and reasoning
  • Artificial Neural Networks

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  • DOI: 10.1007/978-3-030-72651-5_28
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Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

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Correspondence to Henrique Vicente .

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Crespo, A., Lima, R., Martins, M.R., Ribeiro, J., Neves, J., Vicente, H. (2021). An Entropic Approach to Assess People’s Awareness of the Health Risks Posed by Pesticides in Oenotourism Events. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_28

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