Objectives: Identification and adaptation of effective data analysis techniques for the utilization of the huge amounts of data originating from DT-Agro and earth observations. Analysis of DT-Agro results and assessment of current conditions and future scenarios including climate variability and mitigation and adaptation strategies. Participatory design and initial development of spatially explicit agronomical services based on dynamic land evaluation, short-term and seasonal forecasts provided by DT-Agro.
Description of Work: WP5 will identify, evaluate, and elaborate machine learning, deep learning, big data analysis, and data assimilation techniques for the analysis of the huge amounts of data originating from DT-Agro and EO. An assessment of historical and current conditions and future scenarios including climate variability and mitigation and adaptation strategies based on the initial results of DT-Agro application using geospatial analysis technics, statistical methods and deep learning will be then performed. At the same time the characteristics of potential digital, spatially explicit services based on these results will be defined in collaboration with key stakeholders.
