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T1.1 Technical monitoring and coordination |
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DELIVERABLES |
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D1.1 – Technical risk plan |
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D1.2 – Periodic report #1 |
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D1.3 – Periodic report #2 |
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D1.4 – Periodic report #3 |
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D1.5 – Data Management Plan |
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T2.1 Regulatory and ethical framework for the application of fertilizers and phytosanitary products on crops |
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T2.2 Study of agronomic models for the prediction of evapotranspiration, abiotic stress, fungal disease, and pest in woody crops |
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T2.3 Research on geospatial information modelling techniques from agriculture data (e.g., GIS, IoT, Copernicus) |
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T2.4 Research and design of ontologies for the characterization of processes and AI models in the agri-food chain |
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DELIVERABLES |
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D2.1 – White paper on the regulatory framework for fertigation on woody crops |
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D2.2 – List of woody plants and datasets of plant tolerance to biotic and abiotic stresses in relation to specific climatic conditions |
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D2.3 – Ontologies for the characterization of agricultural processes |
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T3.1 Big Data and information fusion techniques from sensor data, multispectral imagery and weather forecasting |
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T3.2 Hybridisation of LP-WAN and WLAN technologies for connectivity in remote agricultural environments |
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T3.3 Cyber-Physical Systems for crop, water and nutrient monitoring and automated fertigation control |
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T3.4 Research of Digital Twins and Multi-Agent Systems for agri-food chain characterisation and simulation |
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DELIVERABLES |
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D3.1 – Edge Computing -based device for Machine Learning model execution and fertigation decision making |
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D3.2 – DTs and VAOs environment for the interaction between fertigation decision-making agents and real-world agricultural entities |
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T4.1 Explainable recurrent neural networks for crop evapotranspiration prediction from IoT and satellite imaging |
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T4.2 Ensemble learning models for predicting crop phenotype and yield according to water and nutrient inputs |
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T4.3 Explainable recurrent convolutional networks for disease prediction from sensorics and multispectral imaging |
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T4.4 Cognitive systems and fuzzy logic for the optimisation of fertigation and crop protection application in crops |
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DELIVERABLES |
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D4.1 – Explainable neural and ensemble models for yield and disease prediction |
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D4.2 – Cognitive engine for fertigation and phytosanitary treatment recommendation |
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T5.1 Definition of evaluation metrics and small-scale implementation |
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T5.2 Experimentation on a small scale |
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T5.3 Results analysis and reporting |
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T5.4 Refinement of algorithms and models |
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DELIVERABLES |
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D5.1 – Report on experimentation results in relevant environment |