Reasoning on Agricultural Data: Integrating metrics and qualitative perspectives


AI tools supporting competitive and sustainable agriculture need to exploit highly diverse kinds of data and knowledge, from raw data provided by sensors to high level expertise knowledge. Taking digitalized agriculture as the targeted application domain, the overall goal of the R4Agri project is to provide a framework for reasoning about knowledge based on heterogeneous data, with a focus on multi-modal and multi-scale sensor data.

Main challenges include context-dependent interpretation of sensor data, which involves reasoning about prior knowledge, and query answering techniques that exploit domain knowledge and accommodate the specificities of data sources in a flexible manner. The application potential in this field of world-wide societal and ecological impact will be demonstrated in realistic use cases.

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