Periodic Reporting for period 2 - AgriBIT (Artificial intelliGence applied to pRecision farmIng By the use of GNSS and Integrated Technologies)
Berichtszeitraum: 2023-01-01 bis 2024-06-30
AgriBIT will improve agriculture chain by delivering higher precision, more accurate and continuously available Precision Agriculture services, combining GNSS, and more specifically new high precision Galileo positioning and augmentation services like EGNOS, Earth Observation (EO) information with on-field and on-machine sensors and actuators, Artificial Intelligence (AI) technologies and expert agricultural knowledge. It will deliver a range of customisable and modular solutions suitable for various types of agricultural uses and brands of crops through six defined objectives:
• Market and needs analysis
• Higher precision location services
• Affordable, European source, high precision Galileo and EGNOSS GNSS receiver
• Bundle of PA services for farmers and farm advisors
• Strategy for services uptake
• Open service-oriented platform
Project objectives are:
• To understand the business needs and translate them to project technical requirements & exploitation strategies
• To develop and provide a bundle of improved precision agriculture services for farmers and farm advisors
• To develop intelligent high precision location farm information management services for efficient precision agriculture applications
• To support the integration of intelligent agricultural analytics and services provided by third parties through an open service-oriented platform architecture
• To deliver an integrated and intelligent approach for services uptake by service advisors and farmers
• To use and improve a high precision GNSS receiver compliant with Galileo and EGNOS in a very diverse set of products and services to deliver affordable solutions
• Market and needs analysis
• Higher precision location services
• Affordable, European source, high precision Galileo and EGNOSS GNSS receiver
• Bundle of PA services for farmers and farm advisors
• Strategy for services uptake
• Open service-oriented platform
Project objectives are:
• To understand the business needs and translate them to project technical requirements & exploitation strategies
• To develop and provide a bundle of improved precision agriculture services for farmers and farm advisors
• To develop intelligent high precision location farm information management services for efficient precision agriculture applications
• To support the integration of intelligent agricultural analytics and services provided by third parties through an open service-oriented platform architecture
• To deliver an integrated and intelligent approach for services uptake by service advisors and farmers
• To use and improve a high precision GNSS receiver compliant with Galileo and EGNOS in a very diverse set of products and services to deliver affordable solutions
In the first period the project started with the organization of the quality management creation of mailing lists for the whole consortium, and the creation of the committees. A Project Management Handbook has been delivered: it provides the AgriBIT partners with the explanation of rules and guidelines to be adopted for the complete management of processes. The coordination actions continued with the monitoring of the whole project tasks.
Regarding the AgriBIT requirements and design, surveys targeting the end-users of pilot sites were conducted to define the technical aspects, interconnections and dependencies among various parts of the system, services and external systems for the agriculture value-added services development.
The first version of the community platform has been completed, containing a presentation of AgriBIT services to end users along with the first version of the APIs interface for third parties.
During the 2nd period, all the components that have been designed and all the sensors foreseen in the use cases have been deployed on the fields.
The consortium has carried out activities related to the definition of the user requirements and the use case scenario of AgriBIT services in cooperation with the end-user partners. At the same time, the state-of-the-art analysis of GNSS and Base Solution Architecture has been completed and the prototype of the custom GNSS receiver has been produced (to be replicated and enclosed after completion of technical validation).
The consortium has developed a set of 9 Precision Agriculture Services (the 8 PA Services originally proposed and one additional service AI based) for the crop growth monitoring, for the P&D early warning system, for the irrigation and tillage scheduling, crop yield estimation seasonal yield prediction, weather services, prescription mapping, UAV guidance and CO environmental values forecast. A toolkit comprising mostly of smartphone applications has been evolved by developing ad hoc functionalities for the project: Demarcator app, LightBar app, User authentication and data acquisition/integration service. The cross platform visualization has been developed to show all the information related to the pilots that have been integrated into the data infrastructure. Moreover the GNSS receiver developed during the project has been integrated into the GNSS Enablers services.
The conceptual architecture for GNSS-enabled service provisioning has been derived and the design of the community platform in terms of infrastructure has been produced.
An effective user-oriented evaluation framework for the project was crafted. A total of 6 weather stations and soil moisture sensors have been sent to pilot sites (2 per country) and 1 brix sensor for the vineyard pilot. 2 custom sensors node for the measurement and the monitoring of the pollution values has been installed in Greece and Italy and their data have been integrated into the Cross-platform visualization.
Moreover, the consortium has worked on the exploitation and dissemination activities through the definition of the exploitation approach and the business proposition. 5 newsletters have been published.
Regarding the AgriBIT requirements and design, surveys targeting the end-users of pilot sites were conducted to define the technical aspects, interconnections and dependencies among various parts of the system, services and external systems for the agriculture value-added services development.
The first version of the community platform has been completed, containing a presentation of AgriBIT services to end users along with the first version of the APIs interface for third parties.
During the 2nd period, all the components that have been designed and all the sensors foreseen in the use cases have been deployed on the fields.
The consortium has carried out activities related to the definition of the user requirements and the use case scenario of AgriBIT services in cooperation with the end-user partners. At the same time, the state-of-the-art analysis of GNSS and Base Solution Architecture has been completed and the prototype of the custom GNSS receiver has been produced (to be replicated and enclosed after completion of technical validation).
The consortium has developed a set of 9 Precision Agriculture Services (the 8 PA Services originally proposed and one additional service AI based) for the crop growth monitoring, for the P&D early warning system, for the irrigation and tillage scheduling, crop yield estimation seasonal yield prediction, weather services, prescription mapping, UAV guidance and CO environmental values forecast. A toolkit comprising mostly of smartphone applications has been evolved by developing ad hoc functionalities for the project: Demarcator app, LightBar app, User authentication and data acquisition/integration service. The cross platform visualization has been developed to show all the information related to the pilots that have been integrated into the data infrastructure. Moreover the GNSS receiver developed during the project has been integrated into the GNSS Enablers services.
The conceptual architecture for GNSS-enabled service provisioning has been derived and the design of the community platform in terms of infrastructure has been produced.
An effective user-oriented evaluation framework for the project was crafted. A total of 6 weather stations and soil moisture sensors have been sent to pilot sites (2 per country) and 1 brix sensor for the vineyard pilot. 2 custom sensors node for the measurement and the monitoring of the pollution values has been installed in Greece and Italy and their data have been integrated into the Cross-platform visualization.
Moreover, the consortium has worked on the exploitation and dissemination activities through the definition of the exploitation approach and the business proposition. 5 newsletters have been published.
As transversal activities, the following activities have been performed for coordination of: the coordination of the execution of the tasks, the organization of the official meetings have been performed, the first amendment, and the organization of the first and the second review
The ethics monitoring for human and personal data requirements has been managed as well during all the first period of the project.
AgriBIT puts emphasis on sustained provision and access of most relevant Earth Observation data, developing enhancements (data products and predictive models) targeting various types of crops out of EO observations to improve agricultural monitoring, development of the capacity and infrastructure necessary to make available and utilize earth observation information.
AgriBIT aims at targeting primarily technological AI challenges in PA, to demonstrate unique benefits behind combining precise positioning offered by modern and future GNSS with advances in Machine Learning/Vision and Artificial Intelligence systems performing cognitive analysis of large amounts of data (Big Data problem) made available from vast variety of sources, from large scale EO images to very localised aerial and ground based surveillance and monitoring systems.
AgriBIT targets development of robust and self-learning (AI-based) means of controlling heterogeneous vehicle systems with focus on Agricultural applications. Starting with nonlinear observer theory to build new efficient algorithms for sensor fusion of inertial, magnetic, range/position, velocity, and imaging sensors, through novel Machine Learning methods to understand the surrounding environments to autonomous self management of a mission. Target detection, tracking and understanding based on AI-vision with be key research subjects.
Many agricultural services on the market are not “smart”, assuming that users are “aware” of all opportunities and what implications certain measurements could have. It is common among farmers to consider technology as a powerful tool.
Whilst PA is a key to sustainable use of resources and decrease the spreading of diseases to crops, current solutions have brought more precise management capabilities to larger farm holdings. This in practice overlooks 70% of farm holdings in Europe whose size is below 15 hectares. AgriBIT provides innovative and advanced farm management services building on the increased accuracy and availability of GNSS enabled services, and its open architecture and approach supports the connection of the services across a wide range of sensors and actuators.
The ethics monitoring for human and personal data requirements has been managed as well during all the first period of the project.
AgriBIT puts emphasis on sustained provision and access of most relevant Earth Observation data, developing enhancements (data products and predictive models) targeting various types of crops out of EO observations to improve agricultural monitoring, development of the capacity and infrastructure necessary to make available and utilize earth observation information.
AgriBIT aims at targeting primarily technological AI challenges in PA, to demonstrate unique benefits behind combining precise positioning offered by modern and future GNSS with advances in Machine Learning/Vision and Artificial Intelligence systems performing cognitive analysis of large amounts of data (Big Data problem) made available from vast variety of sources, from large scale EO images to very localised aerial and ground based surveillance and monitoring systems.
AgriBIT targets development of robust and self-learning (AI-based) means of controlling heterogeneous vehicle systems with focus on Agricultural applications. Starting with nonlinear observer theory to build new efficient algorithms for sensor fusion of inertial, magnetic, range/position, velocity, and imaging sensors, through novel Machine Learning methods to understand the surrounding environments to autonomous self management of a mission. Target detection, tracking and understanding based on AI-vision with be key research subjects.
Many agricultural services on the market are not “smart”, assuming that users are “aware” of all opportunities and what implications certain measurements could have. It is common among farmers to consider technology as a powerful tool.
Whilst PA is a key to sustainable use of resources and decrease the spreading of diseases to crops, current solutions have brought more precise management capabilities to larger farm holdings. This in practice overlooks 70% of farm holdings in Europe whose size is below 15 hectares. AgriBIT provides innovative and advanced farm management services building on the increased accuracy and availability of GNSS enabled services, and its open architecture and approach supports the connection of the services across a wide range of sensors and actuators.