Multiscale Sensing for Disease Monitoring in Vineyard Production
- Project code: COFUND-ICT-AGRI-FOOD-MERIAVINO-1
- Project title: Multiscale Sensing for Disease Monitoring in Vineyard Production
- Acronym: MERIAVINO
- Funding: state budget
- PN III Program name: European and International Cooperation – Subprogram 3.2 – Horizon 2020
- Project type: ERANET
- Realization period: 02/12/2020 – 02/12/2023
- Contract duration: 36 months
- Total contract value: 633.100,00 lei / 130.000,00 Euro
Of which by funding sources:
- Source 1 – from the state budget: 318,001.26 lei representing 65,298.00 euros
- Source 2 – from other attracted sources (contribution own financial): 0.00 lei representing 0.00 euros
- Source 3 – from the European Commission: 315,098.74 lei4 representing 64,702.00 euros
- The contracting authority: Executive Unit for Financing Higher Education, Research, Development and Innovation through the European International Cooperation Program
- Project manager: Associate professor PhD Eng. Mihaela Hnatiuc
Vine disease is a major risk for viticulture, involving economic loss, yield quality reduction and environmental impact when using chemicals for treatment. MERIAVINO project advocates a multidisciplinary approach, which is based on several scientific fields to address the problem of disease and yield estimation in vineyard.
The proposed multi-scale methodology consists of inter-combining and implementing IoT, remote sensing and big data in order to interconnect the vineyard parcels, as well as to develop a non-invasive, eco-friendly and low-cost technology for vine disease detection/warning.
With the goal of reducing economic loss of both quantity and quality, and the environmental impact, various sensors, data fusion techniques, artificial intelligence (AI) with machine learning (ML) methods will be combined along with the development of reprintable sensors for effective vineyard monitoring. The project results are then analysed and geo-visualised on compatible MobApp for end-users for decision-making and early prevention.
Multiscale sensing approach and heterogenous data from vineyards in different countrieswill enable to develop robust decision tools based on AI through:
- H1: Intelligent acquisition methods and processing of Big Data by using dedicated software towards improving production efficiency and operating costs.
- H2: ML approach for finding data patterns enables earlier detection of the vine diseases, and the estimation of yield quality and quantity.
- H3: Earlier disease detection will help winegrowers to reduce phytosanitary chemicals.
Monitoring the vines using IoT technology in the field of Research Station for Viticulture and Oenology Murfatlar, Romania
- implementation of IoT sensors in situ;
- building a network of IoT sensors;
- saving data in Cloud / FOG;
- data security testing;
- develop the models for data processing in the cloud;
- process and evaluation of collected data;
- testing the chosen models on the acquired data in situ;
- Correlation and comparison of results between partners
- Publishing the results in journals, proceeding conferences and applying for a patent
- Annual reports at the end of the stage.
- INSTITUT NATIONAL DES SCIENCES APPLIQUEES CENTRE VAL DE LOIRE – France
- CONSTANTA MARITIME UNIVERSITY-Romania
- RESEARCH STATION FOR VITICULTURE AND OENOLOGY MURFATLAR-Romania
- UNIVERSITY OF WEST ATTICA-Greece
- INSTITUT FRANCAIS DE LA VIGNE ET DU VIN – France