Gaia-AgStream: An Explainable AI Platform for Mining Complex Data Streams in Agriculture

Abstract

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 AI, 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 AI infrastructure. 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.

Costs and Benefits of Sustainability-Oriented Innovation in the Agri-food Industry: a Review

“…However, by partnering with third-party technology companies, the restaurants can reduce the cost of technology adoption and reduce waste while not spending additional money. Schoenke et al (2021) propose the concept of an AI and data streaming platform called Gaia-Ag-Stream, which is a platform for technology development and association that is collaborative in nature, bringing together research centres, industry, agricultural start-ups and farmers to help address the costs, knowledge gaps and technical deficiencies of SMEs in the AI rollout process, and to accelerate the adoption of AI. It will help solve the problems related to costs, the knowledge gap and technical deficiencies faced by SMEs in the process of AI diffusion and accelerate the adoption and diffusion of AI.…”