Agro-STAY: Data collection and analysis of alternative agriculture practices from YouTube

Laura Maxim; Julien Rabatel; Jean-Marc Douguet; Natalia Grabar; Roberto Interdonato; Sébastien Loustau; Landy Rajaonarivo; Mathieu Roche; Maguelonne Teisseire

doi:10.1007/978-3-032-02312-4_6

Acte de colloque

Agro-STAY: Data collection and analysis of alternative agriculture practices from YouTube

Laura Maxim, Julien Rabatel, Jean-Marc Douguet, Natalia Grabar, Roberto Interdonato, Sébastien Loustau, Landy Rajaonarivo, Mathieu Roche et Maguelonne Teisseire

Artificial intelligence and green computing: Proceedings of the 2nd International Conference on Artificial Intelligence and Green Computing ICAIGC 2025, (1589), pp.71-82

Lecture Notes in Networks and Systems

International Conference on Artificial Intelligence and Green Computing (ICAIGC 2025) (Beni Mellal, Morocco, 14/05/2025–16/05/2025)

2025

DOI: https://doi.org/10.1007/978-3-032-02312-4_6

Résumé

Self-suﬃciency

Crisis

Alternative agriculture

Classification

Natural Language Processing

Youtube

To address the current crises (climatic, social, economic), the self-sufficiency - a set of practices that combine energy sobriety, self-production of food and energy, and self-construction - arouses an increasing interest. The CNRS STAY project (Savoirs Techniques pour l'Auto-suffisance sur YouTube) explores this topic by analyzing techniques shared on YouTube. We present Agro-STAY, a platform designed for the collection, processing, querying and visualization of data from YouTube videos and their comments. We propose a full methodology dedicated to processing YouTube videos, and apply Natural Language Processing (NLP) techniques and language models, which enable a fine-grained analysis of alternative agricultural practices described online. In addition, we provide a well-adapted graphical user interface to help the experts analyzing the extracted knowledge.

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Détails

Titre: Agro-STAY: Data collection and analysis of alternative agriculture practices from YouTube
Créateurs - sans rôle: Laura Maxim - Laboratoire Interdisciplinaire Sciences, Innovations, Sociétés
Julien Rabatel - Université de Montpellier
Jean-Marc Douguet - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Natalia Grabar - Centre National de la Recherche Scientifique
Roberto Interdonato - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Sébastien Loustau - Université de Pau et des Pays de l'Adour
Landy Rajaonarivo - Territoires, Environnement, Télédétection et Information Spatiale
Mathieu Roche - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Maguelonne Teisseire - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
Contributeurs - sans rôle: Idrissi Najlae (ed.)
Hair Abdellatif (ed.)
Lazaar Mohamed (ed.)
Saadi Youssef (ed.)
Chakib Houda (ed.)
Erritali Mohammed (ed.)
El Kafhali Said (ed.)
Détails de publication: Artificial intelligence and green computing: Proceedings of the 2nd International Conference on Artificial Intelligence and Green Computing ICAIGC 2025, (1589), pp.71-82
Colloque: International Conference on Artificial Intelligence and Green Computing (ICAIGC 2025) (Beni Mellal, Morocco, 14/05/2025–16/05/2025)
Publications en série: Lecture Notes in Networks and Systems
Note de subvention: Centre National de la Recherche Scientifique;CNRS;FRA;http://dx.doi.org/10.13039/501100004794
Identifiants: 99151819209311
Unité académique: Université de Montpellier
Langue: English
Type de ressource: Conference proceeding
Champs locaux: hal-05384543