The ENVISION solution.pdf

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ENVISION H2020

This document discusses the ENVISION project, which has received funding from the European Union's Horizon 2020 programme. It notes the project aims to develop a platform and services using data collection and analysis to help farmers, payment authorities, and certifying bodies. The project involves partners from several European countries and is currently in stage 1 of requirements gathering. It seeks collaboration from local help centers but will cover travel costs for testing and validation.

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
Legal notice: The ENVISION project and its content reflect only the author’s view, therefore the EASME is not responsible for
any use that may be made of the information it contains!
Ifigeneia-Maria Tsioutsia
AgroApps PC
12/10/2022

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
Shapshot of farmers’ practices
Inspector’s impartiality
A lot of paperwork
Large number of rules
Lack of evidence
Time-consuming
Costly

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
Farmer
▪ Personalised guidance
▪ Boost the performance of
the cultivation
▪ Reduction of administrative
costs
▪ Avoid penalties/ non-
conformances
▪ More transparent execution
of controls

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
PAs and CBs
▪ More targeted on- field
inspections
▪ Continuous monitoring of
farmland
▪ Automated checks based on
EO data
▪ Reduction of costly & time-
consuming procedures
▪ Reduction of operational and
administrative costs
Farmer
▪ Personalised guidance
▪ Boost the performance of the
cultivation
▪ Reduction of administrative
burdens
▪ More transparent execution
controls
▪ Avoid penalties/ non-
conformances

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
STAGE 01
User requirements
Technical
specifications
STAGE 02
Data collection
Data analysis
STAGE 03
Platform and
services
development
STAGE 04
Pilot testing and
implementation
phase
STAGE 05
Current Status
Evaluation,
validation, lessons
learnt and future
pathways
1st iteration
2nd iteration

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
GREECE
DRAXIS, NOA, ETAM,
AgroApps
SERBIA
INOSENS, OCS (CB)
LITHUANIA
NPA (PA)
UK
UREAD, LEAF
SLOVENIA
ITC
BELGIUM
EV ILVO, LV (PA)
CYPRUS
CAPO (PA)

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
User requirements
Business case
Testing
Validation
• No budget
• Travel/ accommodation expenses will be covered by the
Consortium
• Establish collaboration agreements with LHC members

This project has received funding from the European Union's Horizon 2020 research and innovation programme
under grant agreement No 869366.
Ifigeneia-Maria Tsioutsia
AgroApps PC
Email: iftsioutsia@agroapps.gr
Website: agroapps.gr

The document describes the ENVISION project which aims to develop tools for monitoring agricultural practices using earth observation. It received funding from the European Union Horizon 2020 program. The project uses a coproduction approach involving stakeholders from different sectors to develop solutions iteratively. This includes identifying needs, generating ideas, prototyping tools, and testing them with feedback. The goal is to ensure the tools effectively address key challenges in a way that is sustainable beyond the project's lifetime.

Identification of organic farming practices.pdf

ENVISION H2020

This document discusses the use of satellite imagery and machine learning to monitor organic farming practices and help certification bodies conduct inspections. Currently, inspections are costly and time-consuming due to sampling-based on-farm checks. The proposed system would use satellite data on crop growth patterns combined with farmer declarations to continuously monitor all farmland and potentially replace on-site inspections, reducing costs and administrative burden for certifying organic compliance. Machine learning models are trained to distinguish organic and conventional farming signatures using historical satellite and inspection data, and could provide warnings through a traffic light system.

Exchange of knowledge and technology transfer

FERTINNOWA:Transfer of INNOvative techniques for sustainable WAter use in FERtigated crops

First results of the FERTINNOWA presented at "Meeting growers needs: optimal...

FERTINNOWA:Transfer of INNOvative techniques for sustainable WAter use in FERtigated crops

This document summarizes the FERTINNOWA project which aims to improve irrigation and fertilization management for fruit, vegetable, and ornamental growers in Europe. The project conducted a survey of over 500 farming operations across 10 countries to identify bottlenecks and needs regarding water and nutrient use. Key findings included a lack of sustainable water sources, limited monitoring of water and nutrient use, and challenges implementing new technologies. The project is working to address these issues through knowledge sharing between partners and implementing solutions in the field over 1-2 years. The goal is to bridge the gap between available innovations and their adoption by growers.

ENIVISION services in practice - The case of Cyprus and the Cyprus Agricultur...

ENVISION H2020

This document summarizes CAPO's involvement in the ENVISION project, which aims to develop new approaches for checks and monitoring under the New Common Agricultural Policy (CAP). It describes CAPO joining in 2020 when no EU legislation or Cyprus Strategic Plan existed. The project opportunities for adjusting to needs before the 2023 transitional period. Services of interest include crop classification, vegetation/soil indexing, and monitoring cross compliance. The document outlines validation methods and challenges addressed through data exchange and testing toward accurate results. It presents the datacube endpoint service providing on-demand access and a mobile app for geotagged photos to facilitate monitoring.

Envision overview

ENVISION H2020

This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. The project involves partners from Greece, Serbia, Lithuania, UK, Slovenia, Belgium, and Cyprus. It aims to develop a mobile application and Earth observation-derived services to assist in remotely monitoring farmers' compliance with environmental practices, helping to address issues like water pollution, soil degradation, and GHG emissions. The services will work to automate wide-area monitoring throughout the year at lower cost while increasing transparency for public authorities.

Participatory processes for air quality measurements through hackAIR

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Precision crop management

IoF2020

This document summarizes an agricultural precision management tool that combines IoT ground data and satellite imagery to provide near real-time crop monitoring and intelligent nitrogen and water application maps. The tool includes a crop monitoring dashboard displaying daily maps of crop status, soil, and weather conditions. It also includes a water and nitrogen manager that generates application maps and schedules for variable rate application based on monitoring data. These products are aimed at helping farmers detect issues earlier, save inputs and time, and improve yields through optimized management.

This project called MUSHNOMICS aims to optimize mushroom production through data-driven innovation. It will develop AI algorithms to predict mushroom yields, create a digital platform to share data along the supply chain, and design an environmentally controlled modular farm unit. The goal is to lower costs and improve profitability for the mushroom industry through precision agriculture techniques. A consortium of universities and companies from several European countries will carry out production trials, stakeholder engagement, and dissemination activities over three years to implement and validate the MUSHNOMICS system.

Gaps in technology and legislation to reach a sustainable water use

FERTINNOWA:Transfer of INNOvative techniques for sustainable WAter use in FERtigated crops

An automated end-to-end framework for CAP monitoring - Lessons learned from ...

ENVISION H2020

This project received funding from the European Union's Horizon 2020 programme to develop EO data products and services to efficiently monitor agriculture and the environment. Machine learning models are applied to Sentinel-1 and -2 data to create dynamic crop maps and detect potential non-compliance, such as false declarations. Services include crop classification maps, alerts for irregularities, and monitoring of regulations around crop diversification, soil protection, and protected areas. The system aims to reduce fraud and errors while ensuring sustainable agricultural practices.

ENVISION Earth Observation services for CAP monitoring.pdf

ENVISION H2020

This document summarizes work done by the National Observatory of Athens (NOA) for the ENVISION project. NOA developed solutions using Earth observation data from Sentinel satellites to monitor the Common Agricultural Policy (CAP) in Lithuania and Cyprus at national scales. Key solutions included multi-temporal crop type maps, grassland mowing event detection, and compliance monitoring maps. The solutions were implemented using a datacube to store and process satellite imagery at scale. The results supported CAP authorities' decision making and monitoring efforts.

ENVISION: Steps towards developing innovative soil services at a regional sca...

ENVISION H2020

The document discusses a project by ILVO, the Flanders Research Institute for Agriculture, Fisheries and Food, to develop a service for estimating soil organic carbon levels in cropland using satellite imagery. The project aims to simplify monitoring of soil carbon for Common Agricultural Policy requirements. ILVO is collecting soil samples and Sentinel-2 satellite imagery to build models correlating image data with soil carbon measurements. The models will map soil carbon across Flanders to reduce on-site inspections and administration. ILVO is improving their methods by addressing limitations and directly using time series satellite data in their models.

Cessda saw task4-6_haguefocusgrppresentation_0616

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Presentation on the Value and Impact of Social Science Data Archives and the CESSDA SaW Toolkit A set of 38 slides used for the Focus Group Cost-Benefit Funding Advocacy Program (Task 4.6) session at the CESSDA Saw Workshop in The Hague 16/17 June 2016. This was an interactive focus group repeated over two parallel sessions. It was aimed at European social science data archive staff with responsibility for bidding for funding or promotion and advocacy of the archive to key stakeholders. The presentation covers some of the key ideas on how the CESSDA Saw funding advocacy toolkit will be structured, its components, and key facts and approaches it will include. We expect the cost-benefit funding advocacy toolkit under development to support the negotiation with ministries and funding organisations across Europe. The results of the toolkit user requirements survey with responses from 24 European social science archives were presented and discussed, together with suggested approaches and content for the toolkit. 22 people attended the two sessions overall, representing a mix of countries at different stages on the development path for social science archives (none, new/emerging, mature). There was strong interest and support for the emerging toolkit together with open discussion of how it can be applied in the specific political and administrative context of different European countries. The slide set presented here is an extended version including a number of hidden background/ reference slides not used in the presentation. The focus group is one of a series guiding further development of the toolkit and its adoption being given to either: (a) social science data archive staff or (b) their key stakeholders (senior management in their universities, research councils and academies, funding ministries, national statistics offices, research users and depositors). CESSDA is the Consortium of European Social Science Data Archives. The CESSDA SaW project “Strengthening and widening the European infrastructure for social science data archives” is funded by the European Commission as part of its Horizon2020 programme.

Iot and big data technologies for bio industry data bio

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ENVISION H2020

This document discusses an application that was developed to unlock the power of Earth observation big data for users by allowing them to request data from an EO data cube via an interface or API. The requested data can be analyzed in multiple formats. The application utilizes Django and Django Ninja to create a web application and REST API. It retrieves data like Sentinel-2 bands and vegetation indices from the cube and visualizes it. The retrieved data can be used for tasks like monitoring vegetation health. The application is currently being used by CAPO to ingest requested data into their systems to enhance validation processes. There is potential to improve the application by connecting it to an ENVISION database and adding more functionality.

Exchanging of technologies for reducing the environmental impact

FERTINNOWA:Transfer of INNOvative techniques for sustainable WAter use in FERtigated crops

Five techniques for reducing environmental impact in horticulture were showcased: (1) sodium removal from irrigation water using electrodialysis, (2) use of photocatalytic materials to reduce disease and break down plant protection products, (3) phosphorus removal using electrochemical precipitation, (4) particle removal from drain water using filtration without back washing, and (5) reducing nitrate content in soil using a KNS table for fertigation. A demonstration trial in 2017 of strawberry fertigation using a KNS table showed reduced nitrogen content in soil after harvest while maintaining crop growth, production and fruit quality.

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Within-field management zoning

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This project received funding from the European Union to develop a soil map-based variable rate application and machine automation system for potato production. The system includes: 1) A Soil Map Service that creates high-resolution soil maps using sensor data and soil analyses. 2) A Variable Rate Application Map Service that generates precision crop management task maps based on soil variability maps. 3) An Automation & Machine Communication system that automatically translates task maps into machine instructions and logs application data. The goal is to improve potato yields, quality, and farming efficiency through precision agriculture techniques informed by detailed soil data analysis and variable rate applications. Field trials were conducted in 2016-2018 to validate and refine the system.

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ENVISION H2020

Grassland Mowing Events Detection

ENVISION H2020

This document summarizes research on detecting grassland mowing events using satellite imagery. The researchers used Sentinel-1 radar and Sentinel-2 optical data to create dense time series datasets for regions in Lithuania. They developed a fusion model to combine the datasets and overcome issues like cloud coverage. A deep learning algorithm was then used to identify mowing events, including cases with large data gaps. The results showed good detection of mowing compliance based on national regulations. Future work aims to improve the models and generalize the methods to other event detection tasks.

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The ENVISION solution.pdf

1. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. Legal notice: The ENVISION project and its content reflect only the author’s view, therefore the EASME is not responsible for any use that may be made of the information it contains! Ifigeneia-Maria Tsioutsia AgroApps PC 12/10/2022

2. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. Shapshot of farmers’ practices Inspector’s impartiality A lot of paperwork Large number of rules Lack of evidence Time-consuming Costly

3. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366.

4. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366.

5. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. Farmer ▪ Personalised guidance ▪ Boost the performance of the cultivation ▪ Reduction of administrative costs ▪ Avoid penalties/ non- conformances ▪ More transparent execution of controls

6. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. PAs and CBs ▪ More targeted on- field inspections ▪ Continuous monitoring of farmland ▪ Automated checks based on EO data ▪ Reduction of costly & time- consuming procedures ▪ Reduction of operational and administrative costs Farmer ▪ Personalised guidance ▪ Boost the performance of the cultivation ▪ Reduction of administrative burdens ▪ More transparent execution controls ▪ Avoid penalties/ non- conformances

7. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. PAs and CBs Stakeholders ▪ More targeted on- field inspections ▪ Reduction of costly & time- consuming procedures ▪ Reduction of operational and administrative costs ▪ Continuous monitoring of farmland ▪ Automated checks based on EO data ▪ Better control systems based on satellite images ▪ Design more accurate certifications ▪ Data availability, accessibility & re-use ▪ Modernise the monitoring approach Farmer ▪ Personalised guidance ▪ Boost the performance of the cultivation ▪ Reduction of administrative burdens ▪ More transparent execution controls ▪ Avoid penalties/ non- conformances

8. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. PAs and CBs Farmer Stakeholders

9. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. STAGE 01 User requirements Technical specifications STAGE 02 Data collection Data analysis STAGE 03 Platform and services development STAGE 04 Pilot testing and implementation phase STAGE 05 Current Status Evaluation, validation, lessons learnt and future pathways 1st iteration 2nd iteration

10. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. GREECE DRAXIS, NOA, ETAM, AgroApps SERBIA INOSENS, OCS (CB) LITHUANIA NPA (PA) UK UREAD, LEAF SLOVENIA ITC BELGIUM EV ILVO, LV (PA) CYPRUS CAPO (PA)

11. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. User requirements Business case Testing Validation • No budget • Travel/ accommodation expenses will be covered by the Consortium • Establish collaboration agreements with LHC members

12. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. Ifigeneia-Maria Tsioutsia AgroApps PC Email: iftsioutsia@agroapps.gr Website: agroapps.gr

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The ENVISION solution.pdf