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  • About Us
  • Pilots
  • Resources
  • Newsroom
    • Our Community
  • Our Partners
  • Contact Us
  • Home
  • About Us
  • Pilots
  • Resources
  • Newsroom
    • Our Community
  • Our Partners
  • Contact Us

Pilots

Wheat Pilot

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Pilot Leader

AFL

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Biogeographical region of the Pilot

Central Lithuania, Baltic area

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Sector

Arable wheat open field

Range of digital technologies

Data Management Platform, AI-based Digital Twin system, process-based models, AI & Machine Vision, field localization and mapping system, robotic systems (retrofit tractor and Direct Injection Spraying (DIS) system).

Target stakeholders

End-users (farmers, farmers’ associations & cooperatives), agronomists & advisors, agri-food technology manufacturers, AI/Data/Robotics providers, AI, Data and Robotics DIHs, Policy Makers & Standardisation Bodies.

Short description of the Pilot

The SmartDroplets system will be deployed in open wheat fields in central Lithuania in order to evaluate and test its components and their efficiency. The focus of the pilot will be on chemical weeding, nitrogen fertilisation and fungicide applications, utilising state-of-the-art technologies, especially the most relevant advances in AI, Data, and robotics, such as a robotic platform, to perform autonomous waypoint navigation tasks, navigation algorithms, to optimise operation and navigation costs, AI algorithms to supervise the chemical selection and spraying prescriptions, etc.

Major goal

Yield increase by 15-20% per annum, chemical use decrease, operational costs decrease.

Apple orchard

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Pilot Leader

Eurecat - EUT

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Biogeographical region of the Pilot

Spain, Mediterranean

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Sector

Apple fruit

Range of digital technologies

Currently the apple orchard has the Hesperides platform as digital field notebook (https://hesperides.farm/) for data logging, management and fungus prediction. It also has a meteorological station to measure rain, temperature, humidity and wind speed. For each apple variety there are humidity probes in the soil (at three levels), in order to measure the humidity and adjust the irrigation accordingly, once per day. Also, the orchard field has poles every 10.8 meters that are georeferenced with centimeter precision.
In terms of spraying, it is done with human-driven tractors and they are equipped with a GNSS system to provide localization information only.

Target stakeholders

Farmers’ and their associations

Short description of the pilot

The apple orchard has an extension of 130 hectares and produces: Royal Gala, Golden Delicious, Red Delicious, Granny Smith, Fuji and Pink Lady.

In the context of the Smart Droplets project, it is important to highlight that they suffer mainly from 3 types of fungus: Apple Scab (Venturia Inaequalis), Alternaria Mali and Podosphaera leucotricha. All of them are difficult to treat in a curative stage, so prevention is of utmost importance. They all appear with the right combination of humidity and temperature, and rain generates a spreading effect.

To combat these fungus, the digital platform with logged data, together with prediction algorithms based on the RIMpro Apple Scab Prediction Model, suggests when to apply the spraying. Typically, the prediction stage is accurate within just 3 days before the rain, so usually the farmers use this time for spraying. Given the area of the orchard, and the limitation of working hours, it takes 7 tractors working 9 hours per day to cover the whole orchard with these prediction times. They also apply the spraying after it rains.

In this context, the pilot consists of automatizing a tractor in order to perform the spraying operations autonomously, together with sensors to detect the health of the plant to apply the right kind and amount of fungicides, at the right time, in the right place. Also, Smart Droplets will build a digital twin of the farm, with AI based prediction models implemented in order to improve the time and precision of the prediction algorithms, with the final goal of decreasing the appearance of these fungus.

Major goal

Given the descripted operative, the apple orchard waste is, in average, of about 8% of the total production per year (apples affected by fungus).

With the help of more precise AI based prediction algorithms, together with autonomous tractors equipped with sensors to detect the health of the plant in order to apply the right kind and amount of fungicides, at the right time, in the right place, Smart Droplets project expects to decrease the waste.

Also, as the autonomous tractors are able to work 24/7, and they will be able to apply the right amount of fungicides, there are also expectations to decrease the operative expenses and fungicide expenses in a great manner.

Project Coordination
Dr. Spyros Fountas
Associate Professor

Agricultural University of Athens
75 Iera Odos Str. 11855, Athens, Greece

sfountas@aua.gr

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Project Communication
Grigoris Chatzikostas
VP for Business Development

Foodscale Hub

LEONTOS SOFOU 20, THERMI THESSALONIKIS, 57001, Greece

g@foodscalehub.com
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101070496.
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