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Personne :
Lakshmi, Vijaya

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Lakshmi

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Vijaya

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Université Laval. Département de systèmes d'information organisationnels

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ncf13677645

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Résultats de recherche

Voici les éléments 1 - 2 sur 2
  • PublicationAccès libre
    Leveraging the Internet of things and open data to support clean energy in the greenhouse sector : preliminary summary of research findings february 2020
    (Université Laval, 2021-03-23T18:33:51Z) Corbett, Jacqueline; Lakshmi, Vijaya
    The current study, the first within a five-year program of research, focuses on understanding the energy needs of greenhouses, their energy management motivations, energy management practices, and the challenges and opportunities for sustainable energy. From June to October 2020, we interviewed nine people involved in the Canadian greenhouse industry. While we continue to collect data, this report provides a summary of key findings to date. The study explores energy management across diverse greenhouse sectors - vegetables, fruits, and flowers – in two Canadian provinces (Alberta and Ontario). We summarize the main findings below.
  • PublicationAccès libre
    Using AI to improve sustainable agricultural practices : a literature review and research agenda
    (Association for computing machinery, 2023-06-02) Lakshmi, Vijaya; Corbett, Jacqueline
    The world is confronted with the grand challenge of food insecurity amidst growing populations and the climate crisis. Artificial intelligence (AI) deployed in agricultural decision support systems (AgriDSS) raises both hopes and concerns for increasing agricultural productivity in sustainable ways. We conduct a scoping review to uncover the roadblocks to the use of AI-supported AgriDSS in sustainable agriculture. Based on the corpus of 121 articles, we find that the effective use of AI-supported AgriDSS is hindered at technical, social, ethical, and ecological levels. Then, drawing on the experiential learning perspective, we propose how conjoint experiential learning (CEL) can enhance sustainable agricultural practices by enhancing both AI and human learning and overcoming roadblocks in using AgriDSS. Based on this conceptual framework, we build a research agenda that suggests blind spots and possible directions for future research.