01 / Organic Soya yield and protein-content prediction

In the context of this demonstrator, an overall improvement in cultivation and processing phase of organic soya will be achieved, with a focus on yield protein production. The specific financial/business impact include: 1) By separating A class from B class soya, farmers will be able to sell at higher prices, 2) Efficiently manage the application of fertilizers, thus decreasing fertilisation costs and improve the quality and quantity of yields, 3) The ability to plan the transport and storage of the produce, leading to more optimal decision making and cost-reduction, 4) Positive impact on the breeding process, providing knowledge about the quality of soya varieties and good practices on the field, allowing producing more with less, 5) Demonstrate the application of crowdsourcing methods and thus strengthen future similar market applications.

02 / Predictive models for food safety

In the context of this demonstrator, HPC solutions will be utilised in order to assist food safety experts with advanced data analysis and risk prediction for the food supply chain. The specific goals of this demonstrator are: 1) To develop a prediction algorithm that will be able to predict food recalls in the supply chain of various products; 2) To develop a price prediction algorithm for agricultural products; 3) To develop a text classification model for product detection from announcements of food product recalls.

03 / Climate services for organic fruit production

In the context of this demonstrator, the protection of organic fruit from extreme weather events will be investigated. The specific financial/business impact include: 1) Mitigate or prevent damages and injuries in sensitive crops, thus eliminate production losses, 2) Demonstrate the usefulness of satellite and weather data in applied market solutions, and thus strengthen their future business potential, 3) Enable replicability of the solution to other crops and regions, and thus develop new business opportunities.

04 / Autonomous Robotic Systems within Arable Frameworks

The objective of this demonstrator, is to combine minimally sized equipment with a network of ‘actuator’ devices together with an overall management application requiring communications and data analytics for the elaboration of a number of tasks such as soil chemical analysis, hyperspectral imaging (HSI) of soil/crop condition, real time object level (plant/weed) identification, individual plant harvest readiness assessment (particularly for soft fruits) and plant level automated harvesting. The specific financial/business impact include: 1) Decrease the timeframe of several farming tasks and thus increase productivity and mitigate risks (e.g. weather hazards), 2) Decrease equipment size and costs and thus make them more affordable to farmers, 3) Extent the operational window through smaller vehicles and thus improve operational effectiveness and efficiency, 4) Decrease in the labor input needed, 5) Increase overall production.

05 / Optimising computations for crop yield forecasting

In the context of this demonstrator, a crop yield forecasting solution will be further optimized with the aid of HPC and using parcel specific data associated with advanced weather forecasts and computations (weather data interpolation, crop growth model) together with the addition of a third data source: data processed from Satellite Imagery for validation of the parcel specific estimates and on-the-fly calibration of the crop growth model. The overall aim is to predict farmer’s yields more accurately and at a higher spatial resolution than currently possible. The specific financial/business impacts of this include: 1) Improvement of the quality and quantity of the produced yields; 2) Improve resources management resulting in decreased production costs; 3) Decrease in yield damage/losses.

06 / Pigs weighing optimisation

In the context of this demonstrator, the live weight of grower/finisher pigs and individual pigs will be estimated based on video images, while the growth curve estimated by the CNNs in previously developed models for early warning of diarrhoea will be incorporated. The specific financial/business impact include: 1) Decrease in labour costs, 2) Decrease in overall operational costs, 3) Increase operational effectiveness, 4) Increase overall farmer profit.

07 / Sustainable pig production

This demonstrator aims at the improvement of the health and welfare of pigs (for the pig farmers), as well as at the optimisation of pork production and minimization of risks (for Slaughterhouses and pork processors). The specific financial/business impact include: 1) Improve meat quality, 2) Improve meat quantity, 3) Improve the overall welfare and health of pigs, 4) Minimise the risks of boar taint.

08 / Open Sea Fishing

In the context of this this demonstrator, three different data systems that are currently used or developed in fisheries will be showcased/tested, i) a fleet-based application enabling managers to optimise the quota uptake of the fleet and fishers use more precise information about the location of hotspots of fish, ii) an on board database system to optimize operational decisions on board weights of the landings per species group per haul, vessel speed an fuel consumption per hour and tractive power, iii) and a visual-based processing of the catch. The specific financial/business impact include: 1) Decrease in labour costs, 2) Decrease on operational costs, 3) Increase operational effectiveness, 4) Increase quantity and quality of the catch, 5) Improve overall economic performance of the fleet.

09 / Aquaculture monitoring and feeding optimisation

In the context of this demonstrator, image processing technology will be utilized in order to investigate fish behaviour in a deeper level, which combined with other data (e.g. weather information and sensor measurements) will provide an efficient feed management system as well as other useful monitoring information. The specific financial/business impact include: 1) Feeding Costs Decrease, 2) Operational Costs Decrease, 3) Production Costs Decrease, 4) Fish Production Increase.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825355.

© 2019 CYBELE consortium

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