LIFT • Livestock IoT Farming Technology
LIFT • Livestock IoT Farming Technology
LIFT develops an end‑to‑end system that collects, processes and exploits data from primary livestock units using innovative IoT, AI & ML technologies. The goal: earlier event detection, higher productivity and better animal welfare.
Co-financed by Greece and the European Union
LIFT (Livestock IoT Farming Technology) is building a holistic precision‑farming solution for dairy cattle. The system combines on‑animal sensors and environmental stations with a secure gateway and cloud analytics to generate actionable alerts — such as impending calving — enabling timely interventions and improved outcomes.
Provide timely notifications to farm owners, staff or veterinarians for the start of calving so monitoring and assistance can be provided where needed.
Continuously monitor rumen pH and temperature, mobility and rumination as indicators for metabolic issues and overall animal wellbeing.
Leverage continuous data and ML models to support sound decisions that increase milk yield and optimise farm operations and energy use.
A robust gateway with SIM card, antenna and PoE power aggregates sensor data inside the barn and transmits it securely to the cloud. Gateways are installed above animal height for uninterrupted signal.
Two complementary sensor families feed data to the platform:
Station nodes measure climatic conditions as well as CO₂ and PM particulates both inside and outside barns to correlate environment with animal health.
Streaming data is processed to produce dashboards and alerts. Machine‑learning models combine mobility, rumination and temperature features to narrow the window for event predictions and reduce false positives.
The project is organized into thematic work packages covering the entire development and implementation cycle.
Coordination of activities, budget management and communication between partners.
Design and implementation of sensors, gateway and network infrastructure for data collection.
Development of cloud platform, machine learning models and alert system.
Installation of systems on livestock farms and collection of experimental data.
Analysis of results, performance measurement and documentation of benefits.
Publication of results, user training and commercial exploitation plan.
The project brings together academic expertise, industry partners and pilot dairy farms.
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