Gaia-AgStream: An explainable AI platform for mining complex data streams in agriculture

Schoenke Jan, Aschenbruck Nils, Interdonato Roberto, Kanawati Rushed, Meisener Ann-Christin, Thierart Francois, Vial Guillaume, Atzmueller Martin. 2021. Gaia-AgStream: An explainable AI platform for mining complex data streams in agriculture. In : Smart and sustainable agriculture : First International Conference, SSA 2021, Virtual Event, June 21-22, 2021, Proceedings. Boumerdassi Selma (ed.), Ghogho Mounir (ed.), Renault Eric (ed.). CNAM. Cham : Springer, 71-83. (Communications in Computer and Information Science, 1470) ISBN 978-3-030-88258-7 International Conference on Smart and Sustainable Agriculture (SSA 2021), s.l., France, 21 Juin 2021/2 Juin 2021.

Résumé : We present a position paper about our concept for an artificial intelligence (AI) and data streaming platform for the agricultural sector. The goal of our project is to support agroecology in terms of carbon farming and biodiversity protection by providing an AI and data streaming platform called Gaia-AgStream that accelerates the adoption of AI in agriculture and is directly usable by farmers as well as agricultural companies in general. The technical innovations we propose focus on smart sensor networks, unified uncertainty management, explainable Al, root cause analysis and hybrid AI approaches. Our AI and data streaming platform concept contributes to the European open data infrastructure project Gaia-X in terms of interoperability for data and AI models as well as data sovereignty and Al infrastruct ure. Our envisioned platform and the developed AI components for carbon farming and biodiversity will enable farmers to adopt sustainable and resilient production methods while establishing new and diverse revenue streams by monetizing carbon sequestration and AI ready data streams. The open and federated platform concept allows to bring together research, industry, agricultural start-ups and farmers in order to form sustainable innovation networks. We describe core concepts and architecture of our proposed approach in these contexts, outline practical use cases for our platform and finally outline challenges and future prospects.

Auteurs et affiliations

• Schoenke Jan, LMIS AG (DEU)
• Aschenbruck Nils, Osnabriick University (DEU)
• Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0002-0536-6277
• Kanawati Rushed, Université Sorbonne-Paris-Nord (FRA)
• Meisener Ann-Christin, LMIS AG (DEU)
• Thierart Francois, MyEasyFarm (FRA)
• Vial Guillaume, MyEasyFarm (FRA)
• Atzmueller Martin, Osnabriick University (DEU)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/606735/)

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