Periodic Reporting for period 2 - AGRARSENSE (Smart, digitalized components and systems for data-based Agriculture and Forestry)
Periodo di rendicontazione: 2024-01-01 al 2024-12-31
Food security has become an increasing concern due to a mixed bag of factors that include climate change, fragile supply chains and agricultural activity. In fact, the increase in agricultural production to nourish a growing global population has highlighted the need for innovative solutions. With this in mind, the EU-funded AGRARSENSE project will combine the efforts of large enterprises, SMEs and research and technology organisations. To do this, they will develop several technologies like automated agricultural tools and improved sensor technology that will help improve efficiency and protect value chains.
Agrarsense develops advanced hardware and software integrated into systems for new solutions which will be demonstrated in seven Use Cases contributing to solving the challenges outlined. These Use Cases are Improving greenhouse automation (UC1), Vertical Farming (UC2), Precision Viticulture (UC3), Agri robotics (UC4), Forestry (UC5), Optimal Soil & Fertilizers (UC6) and Smart water quality management (UC7).
Agrarsense develops advanced hardware and software integrated into systems for new solutions which will be demonstrated in seven Use Cases contributing to solving the challenges outlined. These Use Cases are Improving greenhouse automation (UC1), Vertical Farming (UC2), Precision Viticulture (UC3), Agri robotics (UC4), Forestry (UC5), Optimal Soil & Fertilizers (UC6) and Smart water quality management (UC7).
The project is entering its most important phase: integration of the technologies in each use case and testing and validation of the demonstrators in the coming growth season, including the forestry testing in northern Sweden as well as the vineyards in southern Italy. One major risk will be the weather conditions; Very dry or rainy weather, and other environmental conditions might have an effect on the demonstrators or data acquisition for the various models and conclusions of the project.
For the greenhouse and vertical farming use cases the work continues. The partly autonomous greenhouse, controlled by algorithms optimally attuned to plant health and needs, robotic systems for both harvesting and greenhouse logistics are being tested and validation of benefits and economic value for customers ongoing.
In the vertical farming use case important experience is gained to make the operation of a vertical farm more efficient and to increase the possibility of producing high quality medicinal plants locally.
Prototypes for Smart Grape DSS have been developed and tested in pilot vineyards. Improved resource use efficiency, productivity and sustainability has been tested and data acquired for disease detection. An agricultural robot platform conscious of its environment and co-workers, capable of actions (ex: pruning & picking) for indoor and outdoor use is being tested.
Preparations for real environment testing and validation of an autonomous shuttle for transport of timber from harvester to the roadside, and an improved ADAS system for forestry data acquisition is ongoing. Sensor, simulations and control logic development are on track.
The soil and fertilizer optimization demonstrations are a promising effort to show that soil conditions can describe the intensity of greenhouse gas emissions in farming.
A smart water quality management system, including sensors, analyzers and decision support system for Recirculating Aquaculture farmers and Irrigation Water Managers for optimizing agricultural yield is being integrated for testing. Also, an autonomous robot boat that can carry water analyzers on reservoirs, rivers, lakes and canals is being prepared.
Videos of the current status and preparations for the coming growth season can be seen for each of the seven use cases on the Agrarsense website: https://www.agrarsense.eu/project-1(si apre in una nuova finestra)
Summary of main achievements (technical and scientific) from technical report.
• Demonstrators operational in Use Cases: Technologies such as the VOC microsensor, LiDAR, Chemical/Biosensor Analyzers, and Multi-level soil moisture sensors are now actively deployed and validated in target environments.
• Validation under real-world conditions: Components including the Plant Monitoring Sensor, LED panel, and Micro-electrodes have demonstrated compliance with performance requirements in field trials.
• Advanced prototypes finalized: All sensor prototypes (e.g. Biological Analyzer, Chemical Analyzer Platform) meet technical specifications and are undergoing final refinements for scalability.
• Field tests started and in planning for coming season, including site visits
• The availability of varied sensor data increased, enabling use in AI models and virtual simulation environments. Several advanced algorithms tested and improved.
• IoT solutions strengthened, ensuring stable real-time data transmission and data-collection.
• Simulation environments expanded through development of digital twins for prototype testing of autonomous machinery.
• Awareness of cybersecurity issues and threats in all use cases has increased
• Videos produced on interim integration, testing and preparations for each use case
For the greenhouse and vertical farming use cases the work continues. The partly autonomous greenhouse, controlled by algorithms optimally attuned to plant health and needs, robotic systems for both harvesting and greenhouse logistics are being tested and validation of benefits and economic value for customers ongoing.
In the vertical farming use case important experience is gained to make the operation of a vertical farm more efficient and to increase the possibility of producing high quality medicinal plants locally.
Prototypes for Smart Grape DSS have been developed and tested in pilot vineyards. Improved resource use efficiency, productivity and sustainability has been tested and data acquired for disease detection. An agricultural robot platform conscious of its environment and co-workers, capable of actions (ex: pruning & picking) for indoor and outdoor use is being tested.
Preparations for real environment testing and validation of an autonomous shuttle for transport of timber from harvester to the roadside, and an improved ADAS system for forestry data acquisition is ongoing. Sensor, simulations and control logic development are on track.
The soil and fertilizer optimization demonstrations are a promising effort to show that soil conditions can describe the intensity of greenhouse gas emissions in farming.
A smart water quality management system, including sensors, analyzers and decision support system for Recirculating Aquaculture farmers and Irrigation Water Managers for optimizing agricultural yield is being integrated for testing. Also, an autonomous robot boat that can carry water analyzers on reservoirs, rivers, lakes and canals is being prepared.
Videos of the current status and preparations for the coming growth season can be seen for each of the seven use cases on the Agrarsense website: https://www.agrarsense.eu/project-1(si apre in una nuova finestra)
Summary of main achievements (technical and scientific) from technical report.
• Demonstrators operational in Use Cases: Technologies such as the VOC microsensor, LiDAR, Chemical/Biosensor Analyzers, and Multi-level soil moisture sensors are now actively deployed and validated in target environments.
• Validation under real-world conditions: Components including the Plant Monitoring Sensor, LED panel, and Micro-electrodes have demonstrated compliance with performance requirements in field trials.
• Advanced prototypes finalized: All sensor prototypes (e.g. Biological Analyzer, Chemical Analyzer Platform) meet technical specifications and are undergoing final refinements for scalability.
• Field tests started and in planning for coming season, including site visits
• The availability of varied sensor data increased, enabling use in AI models and virtual simulation environments. Several advanced algorithms tested and improved.
• IoT solutions strengthened, ensuring stable real-time data transmission and data-collection.
• Simulation environments expanded through development of digital twins for prototype testing of autonomous machinery.
• Awareness of cybersecurity issues and threats in all use cases has increased
• Videos produced on interim integration, testing and preparations for each use case
- Use of large language models (LLMs) to support risk assessments: A prototype system to support experts in performing risk assessments (functional safety and cybersecurity) of machinery. Based on open LLMs and run locally (data privacy).
- Reinforcement learning robot - Using Reinforcement learning for robotic control
- Micropump for improved VOC sensing
- Sensitivity enhancement in GFETs using metal-oxide nanoparticles for improved response to VOC gases and water contaminants.
- Silicon chip based electrochemical sensors for rapid detection of multi analyte on-site sensing
- Using plant electrophysiology signals to drive autonomous irrigation.
- Reinforcement learning robot - Using Reinforcement learning for robotic control
- Micropump for improved VOC sensing
- Sensitivity enhancement in GFETs using metal-oxide nanoparticles for improved response to VOC gases and water contaminants.
- Silicon chip based electrochemical sensors for rapid detection of multi analyte on-site sensing
- Using plant electrophysiology signals to drive autonomous irrigation.