NODES - SPOKE 6 Project FORMIDABILÆ - D5.1 Big data platform and identification of prediction models using temporal data acquired in other RM for resource optimization

The Big Data Platform developed within the NODES project establishes a modular, containerized architecture to integrate, process, and analyze heterogeneous agricultural data for sustainable resource management. It enables automated ingestion and orchestration through Apache NiFi and Airflow, with visualization supported by Superset and Hue. The system connects IoT sensors, blockchain traceability data, and satellite observations, providing real-time monitoring and predictive analytics. Advanced machine learning models enhance biogas optimization, feed quality prediction via NIR spectroscopy, and environmental monitoring through drought, manure, and crop yield detection. The platform supports interoperability, scalability, and secure data governance using SSO and blockchain notarization. Its predictive models improve efficiency in livestock and energy systems, contributing to data-driven innovation in agroindustry. Overall, the system represents a key step toward digital transformation and sustainability in agricultural ecosystems. This document is a deliverable of the project FORMIDABILÆ ("Forage system to make resilient Maize, Dairy and Biogas supply chains for a Lasting Agricultural Ecosystem"), which promotes approaches aimed at improving the smart, resilient, circular and diversified farm to ensure food security and social economic sustainability of dairy food chains with the aim of reducing greenhouse gas emissions in this sector. This document is part of the project NODES which has received funding from the MUR – Missione 4, Componente 2, Investimento 1.5 – Creazione e rafforzamento di “Ecosistemi dell’innovazione”, costruzione di “leader territoriali di R&S'' – del PNRR funded by the European Union - NextGenerationEU with grant agreement no. ECS00000036

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-31
DOI
https://doi.org/10.5281/zenodo.17494972
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NODES - SPOKE 6 Project FORMIDABILÆ - D5.1 Big data platform and identification of prediction models using temporal data acquired in other RM for resource optimization

Zenodo (CERN European Organization for Nuclear Research)
Smart Agriculture and AI
article

NODES - SPOKE 6 Project FORMIDABILÆ - D5.1 Big data platform and identification of prediction models using temporal data acquired in other RM for resource optimization

article en

Abstract

The Big Data Platform developed within the NODES project establishes a modular, containerized architecture to integrate, process, and analyze heterogeneous agricultural data for sustainable resource management. It enables automated ingestion and orchestration through Apache NiFi and Airflow, with visualization supported by Superset and Hue. The system connects IoT sensors, blockchain traceability data, and satellite observations, providing real-time monitoring and predictive analytics. Advanced machine learning models enhance biogas optimization, feed quality prediction via NIR spectroscopy, and environmental monitoring through drought, manure, and crop yield detection. The platform supports interoperability, scalability, and secure data governance using SSO and blockchain notarization. Its predictive models improve efficiency in livestock and energy systems, contributing to data-driven innovation in agroindustry. Overall, the system represents a key step toward digital transformation and sustainability in agricultural ecosystems. This document is a deliverable of the project FORMIDABILÆ ("Forage system to make resilient Maize, Dairy and Biogas supply chains for a Lasting Agricultural Ecosystem"), which promotes approaches aimed at improving the smart, resilient, circular and diversified farm to ensure food security and social economic sustainability of dairy food chains with the aim of reducing greenhouse gas emissions in this sector. This document is part of the project NODES which has received funding from the MUR – Missione 4, Componente 2, Investimento 1.5 – Creazione e rafforzamento di “Ecosistemi dell’innovazione”, costruzione di “leader territoriali di R&S'' – del PNRR funded by the European Union - NextGenerationEU with grant agreement no. ECS00000036

Zenodo (CERN European Organization for Nuclear Research)
Università Cattolica del Sacro Cuore (IT), University of Pavia (IT)
Zero hunger
Openalex Percentile: Top 30%
Smart Agriculture and AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.