Learning agricultural practices-such as gardening, maintaining fruit trees, and general farming techniques-has increasingly shifted towards digital platforms, with tutorials on YouTube being a popular resource. As the metaverse expands, immersive experiences are emerging as powerful tools for skill acquisition. This work introduces AgriMus, a search tool designed for metaverse environments, enabling users to discover both videos and interactive experiences tailored to teaching practical skills in agriculture. AgriMus aims to connect users with relevant virtual spaces where they can learn and practice agricultural tasks in a hands-on, engaging way. Initial experiments conducted on 83 exhibitions demonstrate the potential of zero-shot search methods, achieving 27% R@1, 41% MRR, and 52% nDCG@5. The results also highlight the importance of leveraging the hierarchical structure of exhibition data and integrating state-of-the-art vision-language models to improve search performance. The source code and data of this work is available at https://github.com/aliabdari/AgriMus.
AgriMus: Developing Museums in the Metaverse for Agricultural Education
Falcon A.;Serra G.
2025-01-01
Abstract
Learning agricultural practices-such as gardening, maintaining fruit trees, and general farming techniques-has increasingly shifted towards digital platforms, with tutorials on YouTube being a popular resource. As the metaverse expands, immersive experiences are emerging as powerful tools for skill acquisition. This work introduces AgriMus, a search tool designed for metaverse environments, enabling users to discover both videos and interactive experiences tailored to teaching practical skills in agriculture. AgriMus aims to connect users with relevant virtual spaces where they can learn and practice agricultural tasks in a hands-on, engaging way. Initial experiments conducted on 83 exhibitions demonstrate the potential of zero-shot search methods, achieving 27% R@1, 41% MRR, and 52% nDCG@5. The results also highlight the importance of leveraging the hierarchical structure of exhibition data and integrating state-of-the-art vision-language models to improve search performance. The source code and data of this work is available at https://github.com/aliabdari/AgriMus.File | Dimensione | Formato | |
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