Hay data analytics & fermentation control to increase dairy farm milk yield

Informations projet

HAYTECH AI

N° de convention de subvention: 190159907

DOI

10.3030/190159907

Projet clôturé

Date de signature de la CE 31 Janvier 2023

Date de début 1 Février 2023

Date de fin 31 Octobre 2025

Financé au titre de

• The European Innovation Council (EIC)

Coût total

€ 3 571 250,00

Contribution de l’UE

€ 2 499 875,00

Coordonné par

QUANTURI OY
Finland

Periodic Reporting for period 2 - HAYTECH AI (Hay data analytics & fermentation control to increase dairy farm milk yield)

Période du rapport: 2024-02-01 au 2025-10-31

Résumé du contexte et des objectifs généraux du projet

The HAYTECH AI project (Grant 190159907) addresses a major challenge in the dairy and hay-production industry: nutrient losses and fire risks caused by uncontrolled fermentation in hay bales. Up to 30 % of stored hay can spoil, leading to reduced milk yield, increased waste, and higher CO2 emissions.

Quanturi Oy develops HAYTECH AI, a system combining wireless IoT probes with AI-driven analytics to predict hay quality and identify risks before spoilage occurs. Each probe measures internal bale temperature and transmits the data to a secure cloud platform (HAYTECH.app). Machine-learning models integrate these readings with agronomic and environmental data to deliver accurate predictions of nutritional quality: Crude Protein (CP), Neutral Detergent Fibre (NDF), and Relative Feed Value (RFV).

By enabling farmers to monitor hay condition in real time and plan optimal feeding strategies, HAYTECH AI helps increase milk yield by 1.5 – 2 %, reduces hay losses by ≈ 10 %, and lowers the carbon footprint per litre of milk.

Originally conceived to include a fermentation-control reagent mechanism, the system evolved after field trials and reviewer recommendations into a predictive, preventive approach focused on data analytics rather than chemical intervention. This pivot improved scalability, safety, and market acceptance while preserving the project’s innovative edge.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

By the end of the project, Quanturi finalised all technical, validation, and market activities:

• Hardware & Platform: The probe and base-station architecture were redesigned for scalability and validated. Latency < 60 s, wireless range ≈ 1 km, battery life > 18 months, and > 2 400 probes per base station were demonstrated. Manufacturing cost fell 53 %.

• AI System: Predictive models achieved ≈ 85 % accuracy for CP and NDF values, and the Feed Value Indicator (FVI) was validated as a decision-support tool for ration optimisation.

• Pilots & Market Validation: Ten pilot farms in France, Germany, Italy, and the UK plus field sites in Australia validated usability and agronomic accuracy. Distribution partnerships were established with John Deere (U.S.) and Farmscan (Australia).

• Exploitation & Dissemination: HAYTECH AI was showcased at Agritechnica 2023, World Ag Expo 2024, and numerous webinars and fairs. Five demo days (> 250 participants) and online outreach (+ 45 % audience growth) expanded awareness.

All work packages achieved 100 % completion, and the system reached TRL 8 – System Complete and Qualified.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

HAYTECH AI moves the hay-quality sector from reactive observation to predictive management.
Key results include:

• A validated AI algorithm predicting hay nutritional composition from agronomic data (RMSE ≈ 3 %).

• The Feed Value Indicator, transforming complex forage parameters into a simple score usable by farmers and feed advisors.

• An industrial-grade IoT platform linking hardware, cloud analytics, and user interface within one ecosystem.

• A 50 % reduction in sensor cost, enabling wide adoption in smaller farms.

• A unique, data-driven prevention model reducing hay waste and increasing milk-yield efficiency.

These results push the boundaries of digital agriculture by combining remote-sensing, AI, and livestock nutrition analytics in a single, validated system.

HAYTECH AI