D1.1 - Online technology and use cases outlook Y1

AGRIFOODTEF project July 16th, 2024 1 Horizontal logo EDITOR’S NOTE AND RESUBMISSION OF D1.1 - ONLINE TECHNOLOGY AND USE CASES OUTLOOK Y1 JUNE 2024 Horizontal logo Ref. Ares(2024)6128853 - 29/08/2024 First and foremost, we would like to thank you for your review of the report, which was prepared for deliverable D1.1. We are aware of some shortcomings of this document and the not fully developed vision of the report itself. However, we consider this as a starng point, which allows us and the reviewers to focus on specific aspects and subsequently prepare reports that will be developed each year with the knowledge and experience we acquire during our interacon with SME companies, European Digital Innovaon Hubs (EDIHs) and other relevant stakeholders. During repeated meengs and discussions on the subject, we have come to some conclusions, which we present in the following secons to beer explain the objecves of this report. Purpose of creating and maintaining the catalogue The aim of the catalogue is to present soluons available in commercially available products or tested by agrifoodTEF in the context of specific agricultural technologies and use cases that are based on robocs and AI. In line with the consorum's vision, this document will be largely used internally by the consorum members to modify the scope of their services to meet the needs of the rapidly changing market for newly developed products. However, access to the catalogue will be provided to the public, where, according to our vision, the catalogue's audience will primarily be providers of digital product/services for agri-food farms/companies, which will use it in two ways: they will be able to update it with new technologies, agrifood technologies, funconalies, soluons and use cases, and secondly they will directly benefit from the opportunity to learn about the services that are offered for the soluons in queson and will be inspired by learning from examples of others. Accordingly, there is a need to define the definions on which to base further consideraons. Here, the main concepts are: Technology outlook in AgrifoodTEF - it refers to forecasts and predicons about future trends and digital soluon developments in agriculture. It encompasses both new soluons that are just emerging and exisng soluons that are evolving from new use cases of technology mixes or new technologies. Therefore, AgrifoodTEF will use technology outlooks to:  Idenfy new opportunies and prepare for change: Understanding upcoming technological changes in soluons can help adapt services and infrastructure to those changes and remain compeve.  Make informed investments: Technology forecasts can help consorum members make informed investment decisions about new infrastructure, tools, acquision of new competences to be prepared for tesng changing soluons and new use cases. Technology - a technical soluon that makes a given funconality possible (i.e. proximity lidar sensor, GPS, hyperspectral imaging, 5G communicaon). Agriculture technology / food processing technology - a method of carrying out a producon or process, e.g. autonomous potato harvesng, automac onion peeling. Use case / essenal use case - publicly observed intenon to leverage a new advanced robocs or AI soluon to address a specific problem within the agri-food sector. This intenon is contextualized by the specific condions present like i.e. a given locaon. Soluon - soluon in specific product responding to needs in agri-food sector. It may be either a complete system (e.g., an autonomous robot managing phytosanitary product spraying in a vineyard) or a subsystem (e.g., the percepon system providing that robot with the capability to decide how much product to spray), but the key point is that there exists a very specific problem that the soluon addresses. Funconality -it applies to the basic capabilies of a robot system that the system leverages to perform complex acvies called "tasks". Cluster - concentrated group of similar agriculture technologies or/and soluons. Within the cluster, experiences will be exchanged, and service components will be created such as, methodology, infrastructure, metrics (i.e. crop-harvesng, weeding). Content of the report 1Y The experience we will gain from offering services will have a direct impact on the scope, quality, and quanty of technologies that will be idenfied, described, and analysed. Due to the lack of provision of services by the consorum (problems described in the response to the Report dated 13 June 2024) and the lack of contact with EDIHs in the first year, we could not base our report on informaon obtained from the market. Therefore, we decided to base the first report on the consorum's collecve knowledge of available or developing soluons and to focus on the technologies that are available and used in the largest number of products, so to obtain a general picture of the applicaon of new technologies in the agri-food sector. In the following years, we will develop this database primarily based on public informaon on use cases of new technologies or exisng technologies in a new funconal-environmental context, the services sought around the funconalies and tasks performed by these soluons. In the first year, we therefore focused on: 1. Collecng informaon from the European market on available roboc or AI soluons in exisng products to idenfy available technologies and their popularity. 2. Idenfying the strengths and weaknesses of the idenfied technologies, which may indicate potenal infrastructure and tests needs for validaon. 3. Creang a database of use cases that can be implemented using individual technologies to facilitate the preparaon of test infrastructure for these scenarios by consorum members. Multi-Year Plan for the Development of outlook in the agri-food Sector 2Y. edition The consorum will already have experience from the first successfully implemented services. The awareness of the needs and ways of using individual technologies will be much greater. In addion, a catalogue of available or new soluons in agriculture will be available online both for browsing and for entering new items by all visitors. Detailed work plan with a report for the next year: 1. Research on which technologies are being used in EDIHs for agricultural soluons (surveys), 2. Mapping idenfied technology data and use cases to specific services provided by the members of the consorum to the market, 3. Obtaining informaon from our own consorum’s experience in providing services - primarily concerning soluons, technologies and tested use cases (using i.e. Service Requests descripons), 4. Introducing an online catalogue with submission form (for transparency and classified data security it will need to be based only on publicly available informaon with links to websites), 5. Starng work on clusters that will provide detailed data on the needs within the framework of individual agricultural technologies. 6. We will develop quesonnaires to survey potenal AgrifoodTEF customers to idenfy their specific needs in terms of technologies, services and use cases. 3Y. edition We plan to develop an outlook for new sources of data and knowledge about technologies and use cases. The development of the catalogue will primarily be based on various sources of informaon about the direcon of market development. We plan to: 1. Deepen cooperaon with EDIHs to provide services aer the early TRL stage offered by the hubs and thus 2. Gain knowledge and vision of the development trends of soluons and technologies by creang the possibility of obtaining the opinions of experts who will be surveyors and other industry organizaons 3. In connecon with the development of clusters, a source of informaon obtained by these groups within. 4. Develop a crawler that automacally searches industry portals (locally and globally - provided by the partners) and web sources on the most important social media portals (LinkedIn, facebook, X) and idenfies the occurrence of new use cases with assignment to the locaon - automacally sends a request to the satellite coordinator to refer to this news (new important / known / unimportant, etc.). If important, it is obliged to describe and refer to how TEF responds to the potenal tesng needs of the soluon presented within the use case. The plan and development of the catalogue in the coming years will be subject to modificaons according to the needs expressed by our members and the companies that use the services. There is no doubt that our knowledge and the level of fulfilment will grow as we gain more experience on services and use cases. Please find our response to the exact suggesons in the report: The deliverable provides a generic outlook which is focused on each node. It is unclear what the relevant online technology to be used in the project as a whole is. The report is projected to be updated each year during the project. In the inial phase, AgrifoodTEF used the knowledge and capabilies of the consora members to create an overview of the soluons commercially used. The input provided was used for further analysis. Due to delays in naonal funding, many partners did not start the market research for potenal needs/soluons, technologies and business analycs was not performed by them neither. In view of the above, the report is a first version on which further work will be carried out also capitalizing on the reviewers' suggesons. The report is not focused on each node or satellite but rather tries to idenfy all technologies available across the whole European market. Only later equipped with the knowledge from EDIHs and markets we will be able to analyse members preparaon to serve in tesng with specific soluons or specific local requirements 1. Clarify technological focus: Revise the deliverable to explicitly idenfy and elaborate on the key online technologies that the project intends to leverage. This includes detailing how these technologies will be integrated across various nodes and satellites to achieve the project's overarching goals. The work will be carried out as described above , but the catalogue that was created giving a clear technological grouping of commercially available products along with the technologies used, their mixes and TRLs of the products was extremely necessary to be made. This maps certain soluons used in Agriculture 4.0 that will be leveraged through the services being provided referring to physical services. We need to be aware that companies are very discreet in communicang informaon about the technologies and technical soluons used, hence cataloguing is not a simple process. Informaon must be obtained from other sources (SMEs, EDIHs). What needs to be emphasised is that the product catalogue allowed us to define the basic underlying/available technologies and use cases. It is therefore a starng basis on which we will build soluons and use cases outlook of commercial or quasi-commercial soluons that will be tested with the offered services. As stated before, in the Y2 outlook we will try to focus more on soluons provided and use cases rather than analysing just technologies itself since all agrifood soluons are based on mixed subsystems that need validaon as whole. This does not imply that services focused on subsystems fo direct applicaons will be excluded (i.e."test the performance of this model of LiDAR sensor when used to detect the presence of obstacles in a field of tall grass”). Hence it may be a good starng point. This is not the long term objecve of the outlook. Scoung technologies seems in this maer as something that is not intended to be done by TEFs as they are established to test, and validated soluons already provided by the companies. This acvity should be more on the side of EDIH's, which operate at a lower TRL, where technology can be matched to product or specific use case 2. Enhance analycal depth: Shi the focus from merely cataloguing companies to providing a thorough analysis of the technologies they offer, their readiness levels (TRL), and their applicability to the project's objecves. This should also involve a crical evaluaon of how these technologies can bridge current gaps in agrifood technology and contribute to innovaon within the sector. 3. Improve accessibility and ulity of the catalogue: Develop an online plaorm with advanced search funconalies and a technology submission feature, as planned. This plaorm should not only facilitate easy access to the catalogued informaon but also enable connuous updang and expansion of the database with the latest technological As described earlier, we are aware of the lack of online access to the catalogue but operaonally we were not ready to do this with partners who realiscally started work late due to funding issues and lack of clear vision of its form. The online catalogue will be developed and made available as soon as possible along with a revision of the whole webpage. For the online innovaons. catalogue it is necessary to create ontology of informaon and tree of dependencies with ability of submission funcon be in place. This works has already started and when completed, informaon will be gathered through clusters and other channels. 4. Incorporate Ethical, Legal, and Societal Aspects (ELSA): As ELSA consideraons are crucial for the project's success, ensure these aspects are integrated into the analysis of technologies and use cases. This will help in idenfying potenal challenges and opportunies for innovaon that are aligned with societal values and legal requirements. ELSA Aspects will be developed in a next phase of the project within the work packages dedicated to tackle this part. At this stage we have started to idenfy technologies and essenal use cases which is not enough to incorporate ELSA yet. We believe that ELSA can be included within the analysis first when we start providing first services. Mapped technologies confronted with ELSA and other regulatory standards will be done to pinpoint threads and challenges. 5. Strengthen the link between technology and market needs: Conduct a more detailed market analysis to align technological soluons with actual needs within the agrifood sector. This includes mapping the customer journey for potenal needs and matching the TEF service offerings to these needs, ensuring the project remains responsive to market dynamics and enduser requirements. Analysing wh ich technology trends will maer and applying them to new soluons is a task beyond TEF's acvies, which should focus on tesng and validang soluons where technology has already been applied. What we can do is determine whether the infrastructure and technologies operang within the TEF meet the needs of the idenfied use cases (e.g. for new robocally advanced or AI technologies in the agri-food sector). Within Y2 of the report, such assessment will be introduced for clusters of idenfied technologies. In subsequent years, the idenficaon will be more focused on specific use cases, of which there are too few at this point for clear indicaons of the direcon of development and technology needs in terms of services and infrastructure provided. In conclusion, we would like to express our gratude for your me and dedicaon to reviewing our report. Your comments and insights have been extremely important to us and have helped us to improve the content of the catalogue and focus on its purpose. We have carefully analysed your suggesons and have addressed them in the appropriate places in the report. In addion, we have recognized - for a beer common understanding - the need to define exactly the most frequently used definions and keywords, which could potenally be understood differently depending on the reader. We hope that this leer and the improved report will be useful to beer understand our consorum's vision for the catalogue and its potenal applicaons. We would like to emphasize that this catalogue is only the first step towards creang an informaon base for the sector of advanced technology products for the agricultural and food processing industries. In the future, we plan to expand its funconality with new tools and services that will facilitate the development of services towards the validaon and tesng of these new soluons. Online technology and use cases outlook Y1 101100622/agrifoodTEF AGRIFOOD TEF 2 Project cofunded by the European Commission within the Digital Europe Programme Dissemination Level PU Public X CO Confidential, only for members of the consortium (including the Commission Services) □ CL Classified, as referred to in Commission decision 2001/844/EC □ This document supports Deliverable Number D1.1 Lead Beneficiary L-PIT Deliverable Name Online technology and use cases outlook Y1 Deliverable Description Periodic report on technology online catalogue development. Type R — Document, report Dissemination Level PU - Public Due Date (month) 12 Work Package No WP1 Document Revision History Date Issue Author/Editor/Contributor Summary of main change 19/01/2024 V1 Marek Szychta – L-PIT First document version 25/01/2024 Philippe Martinet Review of the document 30/01/2024 V2 Marek Szychta – L-PIT Corrected after review 28/06/2024 V3 Jorge Barriga Florian Adamczyk Corrected after Commission review 09/07/2024 V4 Jorge Barriga Florian Adamczyk Corrected after internal review (Kees Lokhorst, Albino Maggio) 101100622/agrifoodTEF AGRIFOOD TEF 9 4. Method used The report is the first attempt to analyse the technologies and use cases that consortium members have encountered and are working with. Information for this report was collected in December 2023 through questionnaires and a form that was completed by active members. As an additional source of information, the authors used information available on individual manufacturers' websites and online catalogues compiling agriculture of the future or simply agriculture 4.0. This catalogue includes detailed information about various types of technology, including mobile robots, UAVs, autonomous kits, and specialized tools for tasks such as weeding, tillage, seeding, fertilizing, irrigation, harvest, and livestock management. By technology, we mean the equipment and its systems for operating control, monitoring, collecting and processing data collected in various ways, which together serve to carry out the activities resulting from the assumptions that determine their functionality. Each section elaborates on the specific technologies, their functionalities, use cases, and the technical solutions they employ. Additionally, it highlights the pros and cons of technology, trends regarding technology, and potential services and scope that could leverage these technologies. A graphical diagram of the layout of the catalogue is included in Fig.1 to help understand the idea behind its construction and also to help analyse its compactness. Figure 1. Layout diagram of the catalogue 101100622/agrifoodTEF AGRIFOOD TEF 10 5. Technology development catalogue The following catalogue details technologies related to AI, data and robotic technologies in the agriculture and food sector. As an overarching criterion, a breakdown into functionalities defining the scope and capabilities of the devices and their components has been adopted. For each of the 4 indicated functionalities, the broad commercial ranges of currently used devices equipped with devices, technological systems that allow them to work within the functionality to which they have been assigned are included. The catalogue also contains descriptions of the main advantages and disadvantages, as well as the possibilities and direction of development of devices, systems included in each functionality. 5.1. Functionality: Positioning (guidance) the unit implementing technologies used in agriculture Positioning (guidance) of an agricultural unit (machine + tractor) or autonomous device (tractor, implement carrier, machine) equipped with an agricultural machine suitable for the implementation of agricultural technology. Agricultural technology it applies: Sowing, planting, mechanical weed control, chemical weed control, field and crop cultivation, crop harvesting, General scope of use cases: - Control and determination of the route for an agricultural unit or autonomous device, including straight driving, obstacle avoidance, and turns along the optimal and shortest trajectory in order to achieve maximum efficiency and quality of work of the device and machines. - Obstacle detection. - Positioning of machinery and equipment working in field crops: sowing, mechanical and chemical crop protection, fertilization and crop harvesting. - Positioning of areas with diseased, weakened, heavily weedy plants requiring intervention for other reasons as well. 1. RTK GPS (Real-Time Kinematic GPS) RTK GPS is a high-tech positioning system that corrects errors in regular GPS, giving incredibly accurate location data in real-time. It works by using a network of fixed stations that send correction signals to the receiver. This can provide accuracy down to centimetres, making it ideal for tasks like surveying and precision agricultural. Example of the use of commercial solutions: - Mobile robots (tool carriers):  FarmDroid FD20 (Farmsystems): Fully automatic, solar powered field robot that automates your seeding and plant protection. Use high-precision GPS technology. Used to accurately determine the position of each plant.  Dino (Naïo Technologies): Initial purpose of the Dino is weeding. However, due to its power and accurate driving in practice it's also already putted in action on farms for sowing and (small) tillage jobs. The robot uses GPS RTK for navigation.  Orio (Naïo Technologies): Autonomous tool carrier for vegetables and industrial crops. Orio work in fields with accuracy thanks to its guidance system based on RTK GPS signal.  Oz (Naïo Technologies): Oz works completely autonomously thanks to its RTK GPS guidance system. 101100622/agrifoodTEF AGRIFOOD TEF 11  Ted (Naïo Technologies): Autonomous tool carrier for vineyards. Guidance based on RTK GPS signal. Used for mechanical weeding under the row.  Bakus (Vitibot): Bakus is a viticultural straddle robot that integrates a wide range of modular tools for efficient work in vineyards. The robot uses RTK GPS technology for its movements.  Zilus (SABI AGRI): Zilus is an all-terrain robot its height and width can be adapted to meet every need. Its modular design means it can be adapted to all crops. The robot is equipped with a centimetre-level RTK GPS to ensure precision.  ICS20HD-plug-in hybrid, ICS20E - electric (AutoAgri): Is a multifaceted implement carrier, available in fully electric and plug-in hybrid variants, engineered for eco-friendly and precise farming operations. Primary navigation by GPS with RTK.  haRiBOT (HariTech): The robot can drill holes fully automatic for new plantations. The robot is a universal machine, can also put many other implements, like sprayer, fertilizer, cultivator etc. Navigation and control RTK GPS.  Dot Power Platform (Raven Technologies): The platform drives itself and any attached implement, such as a seeder or sprayer, through a combination of GPS technology and associated precision computer systems.  OMNIPOWER 3200 (Raven Technologies): The powered-up autonomous power platform’s attaches to interchangeable implements, such as a seeder, spreader, and sprayer. The robot uses RTK GPS for staying within geofenced field boundary. - UAV:  L30 Spraying Drone (ABZ innovation): Drone equipped with an advanced flight planning algorithm and optimized downward airflow, offers unparalleled performance in drone technology, providing precision and efficiency in a wide range of applications. Use an advanced RTK system.  Koliber VTOL (BZB UAS): Drone with precision cameras and GPS modules, it is able to inspect individual plants.  HF T72 (Hongfei Aviation Technology Co): The HF T72 is a super large capacity agricultural drone, It can spray 28-30 hectares of fields per hour with very high efficiency, uses smart batteries, and charges quickly. Perfect for large areas of farmland or fruit forests. The drone use GPS positioning function, autonomous flight function, terrain following function. - Autonomous kits: Tools: Fertilizing  LeapCat (BBLeap): Multi-level prescription maps for extreme precision. GPS signals determine the exact location of the sprayer. LeapCat adds the exact location of the booms and nozzles to it. Other: Tools & Data  agroOSA: Automatic control systems for agricultural vehicles with modern solutions based on RTK. Pros of technology:  High precision: RTK GPS provides centimeter-level accuracy, which is essential for precision field work such as seeding, spot-spraying, weeding and fertilizing. Enables the preservation of the established optimum amount of seed sown during punctual seeding as a result of maintaining uniformity in the spacing of seeded rows and the distribution of plants in the row, switching off seeding sections to avoid double seeding, for example. Allows 101100622/agrifoodTEF AGRIFOOD TEF 12 the use of non-chemical methods of destroying weeds in between rows and between plants in the row. Allows precise application of fertilizers at the seed or germinating plant in the row, thus reducing the global amount of fertilizers (chemical elements) applied on the surface of a given field reducing the economic and environmental costs of mineral and organic fertilization processes.  Weather independence: RTK GPS works regardless of weather conditions. Cons of technology:  Cost: RTK GPS systems are expensive both in installation and maintenance.  Dependence on Satellite signal: It requires a stable GPS signal, which can be problematic in areas with high forestation or in deep valleys. (Investment in infrastructure near the application can also causes an increase in costs.) Trends regarding technology:  Cost reduction: Innovations are aimed at reducing the costs of components and implementation. Work is underway to increase the availability of RTK signals in regions with difficult terrain configurations, including terrain obstacles such as high dense trees, ground technical installations.  Developments of different satellite systems. Actions to expand the offer by being able to adapt data from different dispatch systems, both public and commercial. Potential agrifoodTEF services and scope:  Positioning accuracy: Measurement of positioning deviation in various field conditions (e.g., in an open field, near obstacles).  Signal stability: Tests of GPS signal stability under various atmospheric and terrain conditions.  Integration with Robotic, Autonomous, Automatic Systems: Testing RTK GPS integration with real-time agricultural robots, evaluating the effectiveness of automating operations such as seeding or fertilizing.  Comparison with geodetic reference data.  Evaluation of the repeatability of positioning results.  Resistance to disturbances and integration of other, backup positioning sources. 2. GNSS (Global Navigation Satellite System) GNSS is a system designed to determine the exact position of a point or moving object in three dimensions based on the measurement by the receiver of the radio signal's travel time from the satellite to the receiver's antenna. In order to determine the coordinates, it is necessary to process signals from at least four satellites and to know their position during the measurement. Examples of the use in commercial solutions: - Mobile robots (tool carriers):  Slopehelper (PeK Automotive): To navigate on a plantation the robot uses differential GNSS, touch sensors, and radars.  Robotti (Agrointelli): Robotti is an autonomous implement carrier highly suited for precise and accurate plant establishment and plant nursing tasks in row and bed crops for both arable and horticultural production. The robot uses RTK-GNSS positioning system.  Jo (Naïo Technologies): The robot uses RTK-GNSS guidance system to navigate between rows of vineyards.  Tipard 350 (Digital Workbench): Primarily navigates with two RTK-GNSS receivers in combination with an IMU.  Tipard 1800 (Digital Workbench): The Robot works with a dual RTK-GNSS receiver. 101100622/agrifoodTEF AGRIFOOD TEF 13  YT5113A Robot Tractor (Yanmar): The system is based on RTK-GNSS which can utilise signals from multiple global navigation satellite systems (GNSS) and the base station.  Ceol (Agreenculture): The robot uses GNSS RTK for navigation and geo-positioning. The field robot is an inter-row crawler. It can work several hectares per day fully autonomous and its rear tool carrier allows it to tow different tools. Pros of technology:  Wide range of applications: The ability to use anywhere and anytime: GNSS provides a positioning signal from satellites anywhere to determine the position of its user. This allows for navigating autonomous or automatic vehicles on the surfaces of all kinds of plantations or crops.  Flexibility: Weather Independence: GNSS operates independently of weather conditions.  Low cost of purchasing a basic set, receiver. Cons of technology:  Accuracy: Lower precision: achieving large, centimetre accuracy requires the use of touch receivers and the system to make computational corrections based on time data and an additional antenna (base station) on the surface of the crop, plantation.  Interference: Satellite signal dependence: Requires a stable GPS signal, which can be problematic in in areas with high forestation or in deep valleys.  Possibility of occurrence of intentional interference with the system, e.g. by the military. Trends regarding technology:  Cost reduction: Innovations are aimed at reducing the costs of components and implementation.  Integration with other systems: Combining GNSS with RTK technologies and vision sensors to increase precision.  Software development: Improvement of correction algorithms and signal filtering to increase accuracy.  Work on increasing the availability of GNSS signals: In regions with difficult terrain configurations, including terrain obstacles such as high dense trees, ground technical installations. Potential agrifoodTEF services and scope:  Positioning accuracy: Measurement of positioning deviation in various field conditions (e.g., in an open field, near obstacles).  Signal stability: Tests of GPS signal stability under various atmospheric and terrain conditions.  Integration with Robotic systems: Checking the integration of GNSS with agricultural robots in real time, assessing the efficiency of automation operations such as sowing or fertilizing.  Comparison with reference geodetic data.  Evaluation of the repeatability of positioning results.  Resistance to interference and integration of other, backup positioning sources. Tests: 1. Initialization time: Measurement of the accuracy of GNSS positioning in different locations and conditions (e.g. forest cover, valleys). 2. Multisystems: Testing the operation of different satellite constellations (GPS, GLONASS, Galileo) and their impact on accuracy. 3. Initialization time: Measurement of the time it takes to obtain a stable signal after the system has started up. 101100622/agrifoodTEF AGRIFOOD TEF 14 Validation:  Field tests using fixed control points.  Analysis of positioning accuracy compared to industry standards. 3. Simultaneous localization and mapping (SLAM) SLAM is an advanced technique used in robotics and autonomous systems. Its purpose is to allow robots or other mobile devices to simultaneously create a map of an unfamiliar environment and determine their position in that map. It is a technology based on a laser scanner and software that allows it to map its environment while positioning itself in that environment. It is thanks to SLAM that robots can effectively navigate dynamic and often unfamiliar spaces. Examples of the use in commercial solutions: - Mobile robots (tool carriers):  AGV - Automated Guided Vehicle (Atria) is an autonomous vehicle that is used in a variety of industrial processes to move goods before, during or after the manufacturing process, or perform other types of functions such as planting in a field. These vehicles are usually introduced into controlled environments and use sensors such as lasers or cameras to avoid obstacles, such as the workers in the factory itself. There are also AGVs that work by wire-guided  Bonirob (Amazone) - agribot uses videoand laser-based positioning as well as satellite navigation to find its way around the fields and with cameras and computer-based image analysis, it recognizes and classifies plants (Bosch Deepfield Robotics).  Mamut (Cambridge Consultants): Is an AI-powered autonomous robotic platform. Equipped with an array of sensors, Mamut maps and navigates its surroundings without the need for GPS or fixed radio infrastructure. As it travels the rows of a field, orchard or vineyard, cameras capture detailed crop data at the plant level, enabling accurate predictions of yield and crop health. It integrates stereo cameras, LIDAR, an inertial measurement unit (IMU), a compass, wheel odometers and an on-board AI system that fuses the multiple sensor data inputs. This sophisticated blend of technologies enables vehicle to know where it is and how to navigate through a new environment, in real time.  TerraSentia (EarthSense): Robot is a vehicle equipped with multiple cameras and LIDAR sensors that runs around the farmland (level 2 automatic driving), which is important for the width of the stem of the target crop / plant, leaf area index, leaf and stem disease. It is a system that can collect plant characteristic data and measure it with high accuracy.The acquired field data is seamlessly transformed into quantitative and consistent information by machine learning-based analysis.  Dood (EarthAutomations): autonomous robots for agriculture can be paired up with different existing implements to perform farm work 24/7, depending on which implement is attached. Thanks to the 3-point hitch, different equipment can be attached to the back and the robot can be programmed to do field work autonomously. It uses computer vision to navigate, thanks to which it can also detect different pests and diseases on the field. Depending on environment conditions and type of mission, the robot can perform waypoint navigation through RTK-gps or use a pre-built map to autonomously navigate in the field with the aid of cameras and computer vision algorithms like SLAM, AI-based object detection and obstacle avoidance. Using one or more stereo cameras the robot is able to perceive depth and distances.  La Chèvre (Nexus Robotics): Robot is able to recognise crops at all stages of growth. It uses AI to differentiate between weeds and crops and then pulls out weeds that are very close to the crops without damaging the crops. Multiple RTK-gps sensors provide position and orientation for navigation while the robot is scanning the underlying crops and weeds with cameras and depth sensors. With calibration methods, the camera and depth sensor measurements are fused by using 101100622/agrifoodTEF AGRIFOOD TEF 15 SLAM methods. SLAM (Simultaneous Localisation and Mapping) let’s build a map and localise a vehicle in that map at the same time enabling vehicles to map out unknown environments. This local map holds the coordinates of the plants. Pros of technology:  Wide range of applications: Can be used anywhere, anytime: SLAM allows navigation in unfamiliar environments without the need for prior terrain mapping. This makes it possible to navigate autonomous or automated vehicles on the surfaces of any type of plantation or crop.  Flexibility: Can work in unknown environments without pre-loading of terrain data  Adapting to change: The multisystem nature of navigation data collection and processing allows the equipment to react dynamically to changing environmental conditions.  Wide working conditions: allows mapping in restricted areas not covered by GPS navigation. Cons of technology:  High computing power of the system: Correct operation of the system in real time requires very high computing power and correct overlapping of point clouds generated from information acquired from cameras and sensors.  Cost of equipment: achieving the right image depth requires the use of a multi-camera system and considerable computing power, which affects the cost of the entire control system.  Inaccuracy of the position of the autonomous device: in the absence of the possibility to determine the absolute position of the device in the field, its localisation error remains variable, as it is based on the last known position and orientation of the robot. Therefore, in such a case, it is necessary to both determine the position on the map and create a new map fragment for the new terrain.  The possibility of deliberate interference with the system, e.g. by the military. Trends regarding technology:  Cost reduction: Innovation is moving towards lower component and implementation costs.  Software development: Improving signal correction and filtering algorithms to increase accuracy. Potential agrifoodTEF services and scope:  Positioning accuracy: Measurement of positioning deviation under different field conditions (e.g. open field, near obstacles).  Signal stability: Stability tests of sensor and camera signals under different weather and field conditions.  Integration with robotic, autonomous systems: Verification of the integration of the SLAM system with agricultural robots in real time, evaluating the effectiveness of automation of operations such as sowing or fertilization.  Comparison with geodetic reference data.  Immunity from interference and integration of other, backup positioning sources Tests: 1. Accuracy and range: Measurement of positioning accuracy with the SLAM system in different locations and conditions (e.g. woodland, valleys). 2. Initialisation time: Measurement of the time it takes to obtain a stable signal and accurate positioning of the device, depending on the available number of cameras, sensors and computing power once the system is up and running. 3. Compatibility of the control equipment making up the system: Interoperability tests of the object position control, terrain and obstacle recognition and terrain mapping devices that make up SLAM-type systems. 101100622/agrifoodTEF AGRIFOOD TEF 16 Validation:  Field tests using fixed control points.  Analysis of positioning accuracy compared to accepted standards.  Analysis of the correctness of autonomous vehicle guidance. 4. Multisystem positioning (mix of different positioning and localization technologies) e.g. RTK-GPS + RTK-GNSS Multisystem positioning is the combination of the functionality of two or more different positioning systems, e.g. RTK+GPS, to achieve high real-time accuracy. The RTK system determines corrections to the GPS system to compensate for positioning errors. Examples of the use in commercial solutions: - Mobile robots (tool carriers):  Automato Robotics Sunjer A2 (Directed Machines): The robot has highly precise RTK-gps/GNSS for navigation.  Land-A2 (Exobotic Technologies): Navigation system with dual antenna RTK-gps, LiDAR sensors to detect obstacles in the vicinity of the robot.  Traxx (Exxact Robotics): Is a robot specialised for narrow vines with navigation system with RTKgps and LiDAR sensors to detect obstacles. It offers a variety of use (tillage and spraying).  Oxin (Smart Machine): Is a fully autonomous multitasking robot for safe, efficient, and sustainable vineyards and orchards. The robots drive pre-planned missions using LiDAR and camera for real time driving adjustments and implement positioning and control. Mission and block information is logged by customers using RTK-gps logging devices to create base line drive paths and identify in orchard hazards and block boundaries.  Swarmbot 5 (SwarmFarm): Robot platform for horticulture, orchard and large-scale agriculture applications. Navigation system with 2cm RTK GPS with IMU and wheel odometry. Obstacle detection 3D Lidar and 3D time of flight infrared cameras.  Herbicide GUSS (Guss): Robot developed for spraying fruit trees. GUSS uses a sophisticated combination of GPS, LiDAR, vehicle sensors.  mini GUSS (Guss): Uses a combination of GPS, LiDAR, cameras and the latest technology to autonomously roll through the orchards, day or night spraying row after row.  Trektor (Sitia): Robot is guided by GNSS and RTK navigation.  Robot One - V2023 (Pixelfarming Robotics): Navigation system with dual RTK-gps and camera sensing.  Agbot 5.115T2 (AgXeed): The robot uses RTK GNSS for precise guidance and safe positioning: ± 2,5cm Communications module for bidirectional data transfer and RTK correction.  Agbot 2.055W3 (AgXeed): The robot uses RTK GNSS for precise guidance and safe positioning: ± 2,5cm Communications module for bidirectional data transfer and RTK correction. Can be used for crop protection, weed control, the application of liquid nutrition and for mowing and shredding prunings.  Agbot 2.055W4 (AgXeed): The robot uses RTK GNSS for precise guidance and safe positioning: ± 2,5cm Communications module for bidirectional data transfer and RTK correction.  Agbot 5.115T2 (AgXeed): The robot uses RTK GNSS for precise guidance and safe positioning: ± 2,5cm Communications module for bidirectional data transfer and RTK correction. 101100622/agrifoodTEF AGRIFOOD TEF 17 - Autonomous kits: Tools: Smart Weeders  Welaser (Pixelfarming Robotics): The robot use the guidance solution with GNSS/RTK positioning. Pros of technology:  Flexibility: Can be customized and adapted to the specific needs of agricultural applications.  Integration: Ease of integration with other positioning and navigation systems.  Precision: Can offer greater data accuracy as a result of synergy of data from different sources, devices. Cons of technology:  Environment dependence: Effectiveness can depend on environmental and topographical conditions.  Compatibility: combining data from many different sources, transmitted using different protocols, can lead to errors and positioning inaccuracy in reading the data or inability to read the transmitted data.  Cost: putting together different positioning devices into a single system can lead to increased costs due to, among other things, the need for devices with significantly higher computing power. Trends regarding technology:  Multisensor integration: Combining different positioning technologies to increase precision and reliability.  Algorithm development: Improving data fusion algorithms and error correction. Tests: 1. Accuracy and consistency: Measurement of positioning accuracy in different environments (e.g., open field, near buildings). 2. Integration with Infrastructure: Testing the integration of different positioning and guidance technologies combined into a system, such as GNSS or RTK GPS, Lidar, evaluating the improvement in accuracy of the overall system performance. 3. Energy Efficiency: Evaluation of the energy consumption of positioning systems and their impact on the working time of robots 4. Compatibility: Compatibility tests, cooperation of systems forming one positioning and guidance system installed on the tested autonomous vehicle. Validation:  Comparison with positioning results of other systems.  Analysis of consistency of results under different operational conditions.  Analysis of compatibility of systems within the system, 5.2. Functionality: Recognition of objects in space, area mapping Object recognition and terrain mapping aim to support the functionality of autonomous agricultural units, as well as harvesters and self-propelled agricultural machinery equipped with agricultural devices for the implementation of agricultural technologies, leading to obtaining the required precision, efficiency, quality of work and obtaining measurable environmental (natural, ecological), economic and social benefits. Object recognition systems are also used to assess the location and condition of livestock, the state of cleanliness of livestock buildings, or the control of feed distribution in feed corridors. Recognized objects also include crops, weeds, crops waiting to be harvested: fruit, 101100622/agrifoodTEF AGRIFOOD TEF 18 vegetables, potato ridges, beet rows or cereal patches. The range of objects recognized in this way also includes any obstacles in the fields and in the driving paths of autonomous machines. General scope of use cases: - Positioning of areas with diseased, weakened, heavily weedy plants requiring intervention for other reasons as well. - Positioning of crop sowing. - Obstacle detection. - Positioning of animals and assessment of their physical condition. - Assessment of the cleanliness of livestock buildings and the status of feed distribution in feed corridors. - Positioning of feed or excrement areas of concentration for their appropriate assignment to animals or removal from the livestock building. - Segregation of images and facilities to assess yield levels. Agricultural technology it applies: Sowing, planting, mechanical weed control, chemical weed control, field and crop cultivation, crop harvesting, feeding, removal of droppings 1. Vision Sensors Vision sensors as a combination of different types of sensors (RGB, multispectral, thermal, 2D, 3D, etc). It is an array of cameras and sensors for image and signal capture, information transfer and the software responsible for image processing and analysis. Examples of the use in commercial solutions: - Mobile robots (tool carriers):  Odd.bot (Odd.bot): Fully autonomous weeding. Recognizing weeds using computer vision.  Andela Robot Weeder (ARW-912) (Andela Techniek & Innovatie): Weed recognition with RGB camera.  WEAI (Ekobot): Recognizing weeds using an RGB camera.  Agri Jacobus (AGROCOM POLSKA): Recognize weeds in real time using a vision system in row crops.  Robotti 150D (Agrointelli): Camera systems allows it to view the crop row. Using an RGB color profile, the technology distinguishes plants from weeds.  Valera (Ant Robotics): Stereo cameras for navigation and obstacle detection, nearfield sensors for obstacle detection, safety shutoff.  HammerHead (FieldRobotics): Inside the orchard it uses only data coming from laser scanner and cameras to navigate inside the orchard.  Prospr (Robotics Plus): Navigation with a combination of vision systems (LiDAR + cameras), for intelligent obstacle detection and avoidance.  MK-V (Robotics Plus): Use the latest computer vision technology to cultivate the land with greater precision and accuracy.  Robovator (F. Poulsen Engineering): The robot is equipped with a special plant detection camera above each row of crop.  RoboWeeder (Smart Farm Robotix): Solar powered weeding robot, use Artificial Intelligence image recognition to spot the weeds among desired plants and utilize smart sensors to provide our robot with self-navigation in and around the field.  Titan FT-35 (FarmWise): Is an automated mechanical weeder that distinguishes crops from harmful weeds using computer vision. Is adaptable to different crops, soils and growth stages. 101100622/agrifoodTEF AGRIFOOD TEF 25 3. Multisystems for object recognition and mapping (Mix of different technologies: vision sensors, LiDAR sensors) Multisystems for object recognition and mapping are a combination of the functionality of two different systems (vision and LiDAR technology) to increase the precision and quality of the data acquired. Examples of the use in commercial solutions: - Mobile robots (tool carriers):  Land-A2 (Exobotic Technologies): Navigation system with dual antenna RTK-gps, LiDAR sensors to detect obstacles in the vicinity of the robot.  Traxx (Exxact Robotics): Is a robot specialised for narrow vines with navigation system with RTKgps and LiDAR sensors to detect obstacles. It offers a variety of uses (tillage and spraying).  Oxin (Smart Machine): Is a fully autonomous multitasking robot for safe, efficient, and sustainable vineyards and orchards. The robots drive pre-planned missions using LiDAR and camera for real time driving adjustments and implement positioning and control. Mission and block information is logged by customers using RTK-gps logging devices to create base line drive paths and to identify in orchard hazards and block boundaries.  Swarmbot 5 (SwarmFarm): Robot platform for horticulture, orchard and large-scale agriculture applications. Navigation system with 2cm RTK GPS with IMU and wheel odometry. Obstacle detection 3D Lidar and 3D time of flight infrared cameras.  Herbicide GUSS (GUSS): Robot developed for spraying fruit trees. Use multiple precision weed detection sensors target and spot spray weeds on the orchard floor, which reduces material usage and drift during application. GUSS uses a sophisticated combination of GPS, LiDAR, vehicle sensors.  mini GUSS (GUSS): Mini GUSS is designed specifically for vineyards and high-density orchards. Uses a combination of GPS, LiDAR, cameras and the latest technology to autonomously roll through the orchards, day or night spraying row after row.  Amos A3/A4 (Amos Power): Navigation sensors include GPS, LiDAR and stereo vision. Obstacle detection is conducted via stereo vision, LiDAR and radar.  Burro (Augean Robotics): Computer vision system and AI plus high-precision GPS.  Prospr (Robotics Plus): Is a robust and autonomous multi-use hybrid vehicle that increases efficiency across a variety of crop tasks. A combination of vision systems (LiDAR + cameras), for intelligent obstacle detection and avoidance.  Harv B8 (Harvest CROO Robotics): Strawberry harvesting robot. The robot navigates via GPS waypoints with Lidar and camera assistance that also does obstacle detection and collision avoidance.  Compact S9000 (AVL Motion): The robot harvests fully autonomously by following the bed and detecting the asparagus without touching the bed. Based on image recognition and 12 harvesting modules, the robot is able to identify the location.  Welaser (Pixelfarming Robotics): The robot uses the guidance solution with GNSS/ RTK positioning. - UAV:  EA2021A (EAVision): The drone has a binocular vision system, which can automatically avoid obstacles during operation. Can follow ultra-low terrain and is also well equipped for hilly and mountainous scenes. The drone is equipped with a Lidar sensor, a millimetre-wave radar sensor and an ultrasound radar sensor. It is suitable for safe and secure night operations. 101100622/agrifoodTEF AGRIFOOD TEF 26  EA-30X (EAVision): The EA-30X is an all-terrain drone for automatic spraying, suitable for complex terrains and crops. The drone has a binocular vision system and can automatically avoid obstacles during operation. - Autonomous kits (tool carriers): Tools: Smart Weeders  Karl (KUHN): GPS system and camera-controlled weeders for maize and sugar beet. Thanks to satellite guidance and multiple sensors (Lidar), it can independently detect changing conditions, implement corrective actions and adjust its operating parameters according to the soil conditions and vegetation stage of the crop. Pros of technology:  High efficiency and quality of transmitted data. Real-time data analysis allows for quick response,  Independence from lighting: Fulfil their function both day and night, without interferences caused by shadows, sunlight or blinding by the light of oncoming vehicle headlights,  Speed of data analysis: The analysis of measurement results is much simpler and faster in their case than for visual images, if, of course, only object detection and not object classification is taken into account,  Adaptability to change: The multisystem nature of navigation data collection and processing allows devices to respond dynamically to changing environmental conditions,  Wide operating ranges: allows mapping in limited areas not covered by GPS navigation.  Exact mapping of a landscape or field: that allows autonomous vehicles to accept or verify previously obtained driving traces. Cons of technology:  High computing power of the system: Proper real-time operation of the system requires very high computing power and proper overlapping of point clouds generated from information acquired from cameras and sensors;  Device costs: Obtaining the right depth of image requires the use of a multi-camera system and considerable computing power, which affects the cost of the entire control system.  Inaccuracy of the position of the autonomous device: In the case of inability to determine the exact location of the device in the field, its localization error remains variable, since it is based on the last known position and alignment of the robot. Therefore, in such a case, it is necessary to both determine the position on the map and create a new section of the map for the new terrain,  Possibility of intentional interference with the system: For example, by the military,  Air humidity impact: Lasers, especially those with a wavelength of 1550 nm, which provide a more accurate measurement, are more dispersed by moisture in the atmosphere. Therefore, in case of adverse weather conditions, problems with accuracy can be expected and one has to reconcile with lower scanning resolution,  Possibility of eye damages: Laser beams, especially those with a wavelength of 905 mn, can damage the lenses of the eyes if they come within the field of vision of the eye,  Inability to recognize colours and interpret inscriptions: This can cause problems in identifying obstacles, assessing the health of plants,  Weight and complexity: The combination of several systems leads to significant complexity in their integration with robotic systems and it increases the weight of the equipment (devices and their additional elements) that must be mounted on devices, vehicles that are to be controlled. Trends regarding technology:  Cost reduction: Innovations are aimed at reducing the costs of components and implementation.  Machine Learning: Improving the functions and functionalities of systems, adapting to variable conditions and non-standard applications, such as recognizing specific patterns. 101100622/agrifoodTEF AGRIFOOD TEF 27  Standardizing data transfer from different devices. Potential agrifoodTEF services and scope:  Signal stability: Tests of signal stability transmitted to receivers under various atmospheric, terrain conditions, and operating conditions (dustiness, haze).  Integration with Autonomous Systems: Verification of the integration of Lidar systems with autonomous agricultural vehicles for their autonomous navigation over the area of fields, crops, plantations. Tests: 1. 3D Mapping: Creating 3D terrain maps and comparing them with reference data. 2. Obstacle detection: Testing the effectiveness of obstacle detection in the field under different conditions (e.g., different vegetation types, terrain slopes). 3. Autonomous navigation: Testing the navigation precision of autonomous robots using LiDAR under real field conditions. 4. Compatibility: Compatibility tests, the cooperation of systems forming one positioning and guidance system installed on the tested autonomous vehicle. Validation:  Comparison of 3D maps with geodetic maps.  Analysis of the correctness of obstacle detection in different scenarios.  Analysis of the correctness of determining and maintaining the path for proper passage of an autonomous vehicle based on visual data. 5.3. Function: Assessing environmental conditions The ongoing monitoring of environmental conditions in areas used for agriculture, cultivated fields, meadows and pastures, vegetable crops, flowers, special plants, shrubs and fruit trees, etc. allows for the implementation of all treatments at the most favourable time and conditions due to often rapidly changing weather and environmental conditions. Continuous monitoring of environmental conditions, including soil, allows for constant updating of resource maps, which, together with yield maps, are used to draw up and update application maps, i.e. digital maps, indispensable for controlling the operation of machinery when performing such operations as fertilization, sowing and crop protection, for example. General scope of use cases: - Measurements of soil quality parameters. - Measurements of atmospheric parameters. - Monitoring of soil condition, its moisture, temperature. Agricultural technology it applies: Sowing, planting, field and crop cultivation, crop harvesting 1. Soil humidity sensors (examples of sensor types and applications) Environmental sensors are a broad category, encompassing all sensors capable of monitoring the quantities that characterise weather conditions or the microclimate indoors. Among other things, they are used to detect 101100622/agrifoodTEF AGRIFOOD TEF 28 physical changes in the environment. An example type of sensor for monitoring soil moisture and other environmental parameters is included. Examples of the use in commercial solutions: - Autonomous kits: Tools: Irrigation  IDROSAT, IDRONE (IDROBIT): IoT solutions for smart irrigation. The system includes: Irrigation scheduling controlled by weather sensors, Flow control and automatic watering calculation for each irrigation sector.  FARMSENSE (Farmunited): The measuring station provides you with qualified data on the current soil condition as well as the climate and air conditions, which you can use as a basis for more precise irrigation, sowing, fertilization and plant protection measures. Others: Tools & Data  Farm Management Software (AGRIVI): An easy-to-use farm management software designed to support farmers in making precise agronomic decisions based on real-time field insights and simplifying farm administration. Real-time microclimate insights straight from fields.  FieldView (Climate): The FieldView platform is the central hub of digital farming innovation, providing you the choice to access to a broad and interconnected set of tools - from aerial imagery, to insurance, soil analysis and more.  SatAgro Platform (SatAgro): A wide range of services based on the use of remote sensing in Poland. Software for farmers. Satellite crop monitoring makes it easy to check the current condition of crops in individual fields and plan agronomic treatments. It allows us to observe the variation of plant condition within each field - from sowing to harvesting.  AGRONETPRO (Agronetpro): Mobile sensors to create your own weather station - the app will provide all the information you need to make the right decisions (wet leaf sensor, humidity and temperature sensor, soil moisture sensor, rain gauge, disease model, weather stations).  FarmCloud (Agri Solutions): Farm management systems based on the framework AgriDATA software platform.  AGRIVI Farm Management Software (Agrivi): Farm management software designed to support farmers in making precise agronomic decisions based on real-time field insights and simplifying farm administration. Real-time microclimate insights straight from fields.  Agro-weather station and sensors (Weenat): A range of up to 8 connected agro-weather sensors to be installed in the farm and linked AI-based predictive tools (such as prediction of water stress or frost).  Agro-weather station and sensors (Sencrop): A range of up to 6 connected agro-weather sensors to be installed in the farm and linked AI-based predictive tools (such as prediction of water stress).  Rolnictwo Precyzyjne Andrzej Przeperski; (Precision Farming Andrzej Przeperski): soil scanning, soil sampling. Pros of technology:  Precision: The ability to obtain precise data on nutrients, pH, moisture, and other key soil parameters,  Fertilizer optimization: Gaining the ability to adjust fertilizer levels to the specific needs of the soil, resulting in better yields, thereby avoiding over-application of fertilizers and crop protection products, reducing production costs, 101100622/agrifoodTEF AGRIFOOD TEF 29  Personalized maps: Obtain soil maps showing the current soil diversity in a given field. Based on these maps, crop placement, fertilization and other work can be planned with greater precision. Cons of technology:  System costs: Quite high costs of purchasing system components.  Sampling time: The sampling time for a single soil sample ranges from a few to several minutes, depending on the size of the plot and its configuration. For large plantation/crop areas and multiple samples, it can take up to several days. Trends regarding technology:  Cost reduction: Innovation is moving in the direction of reducing component and implementation costs. Potential agrifoodTEF services and scope:  Soil quality: Soil quality tests to verify other methods used for surface soil quality assessment in cultivated fields,  Integration with other applications: Verify the use of research results in other applications that show the abundance of fields and supervise the operation of cultivation, seeding or fertilization or irrigation machinery. Tests: 1. Field Mapping: Creating field maps for the purpose of conducting soil quality measurements, comparison with reference data. 2. Sampling and analysis: Efficiency tests and time of soil sample collection in the field under various conditions (e.g., different types of vegetation, terrain slopes) and their transfer for analysis. 3. Measurement, evaluations of weather components: Measurements of selected weather components. (humidity, temperature, cloudiness, etc.) in real field conditions. Validation:  Comparison of maps with geodetic maps.  Analysis of the correctness of sampling and sampling time in different scenarios. 5.4. Functionality: Assessing individual and group characteristics of living animals and environmental conditions in their places of residence and life. The ongoing control of the environmental conditions in livestock buildings, outbuildings, their associated areas (paddocks) and the areas where the animals are housed allows the farm to be managed in accordance with the principles of animal welfare and broad control along with proper supervision over the herds of owned animals. Optimizing environmental parameters in buildings where animals stay allows to provide them with living conditions at the required level, and controlling their health parameters in turn allows for the detection and diagnosis of diseases and other health problems at the early stages of their occurrence. Ensuring and maintaining proper living conditions for livestock and ongoing adaptation to often rapid weather changes is critical for their proper well-being, which in turn leads to their greater efficiency and productivity, and consequently to obtain healthier and in greater quantity livestock products. Another functionality as it fits into this technology is the control of the location of animals in the paddocks around livestock buildings and in pastures. 101100622/agrifoodTEF AGRIFOOD TEF 30 Agricultural technology it applies: Feeding, removal of droppings from livestock buildings, control of environmental conditions in livestock buildings, control of livestock welfare General scope of use cases: - Measurements of humidity, temperature, chemical composition of the air, - Monitoring the health and growth status of animals, their anatomical and physiological parameters, - Supervision over the amount of consumed feed, - Supervision over the cleanliness of livestock buildings, - Control of the location of animals, - Implementation of the milk collection process (automatic milking), - Implementation of the egg collection process. 1. Livestock handling tool Sensors use to livestock handling tool as a combination of different types of sensors and utility technologies. Drones, robots, cameras or sensors that monitor physical activity are devices used by owners to help with pet care. They make it possible to quickly solve problems, make decisions or highlighting patterns of animal behaviour. Examples of the use in commercial solutions: - Mobile robots  Mestrobot RS450 (DeLaval): Manure robot. The scraper robot for slatted floors is equipped with features to help maintain hygiene in the barn.  Optimat (DeLaval): Robotic Feeding. Automatic feeding systems relieve the burden of all routine tasks, while ensuring regular feeding of fresh feed.  Astronaut 5 (Lely): Robotic milking. With the Astronaut model, we have reduced the number of highspeed movements, creating an energy-efficient system.  Discovery 90 S/SW (Lely): Manure robot. The fecal removal robot cleans the barn floor continuously: 24 hours a day, seven days a week. It works at predetermined hours and travels the route you designate without disturbing the cows. It makes the barn a clean, safe and friendly environment.  JOZ Tech Manure Robots (JOZ Tech): The robot offers a range of manure management robots. They use advanced sensor technology to navigate and clean efficiently, ensuring a high level of hygiene in the barn.  Vector (Lely): Robotic Feeding. Automatic feeding according to the needs of each group of animals is precision feeding.  Juno (Lely): Robotic feeding. Increasing the frequency of feed pick-up pays off - pick-up encourages cows to take feed frequently throughout the day and night, which increases feed intake. - Autonomous kits: Tools: Livestock management  Smart Connected weight scale (Digitanimal): An intelligent weighing scale for cattle, sheep, goats or pigs that, without affecting animal behavior.  Auto-weighing crush Liberty PM 6000 (Maréchalle Pesage): Weighing crate operating in complete autonomy. Weights recording with numbers of electronic ear tags.  Weights recording with numbers of electronic ear tags.  Chronopature (Adventiel): GPS collars for cows. Automatic acquisition of data to track the time spent in pasture by the cows. 101100622/agrifoodTEF AGRIFOOD TEF 31  Peek Analytics/ Peek Eleveur (Copeeks): Monitoring system for cows, pigs and poultry. The system relies on a camera and temperature, humidity, carbon dioxide (CO2) and ammonia (NH3) sensors, placed in the barn.  Diagno'PEEK (Copeeks): The Diagno'PEEK offer is made up of mobile equipment backed by a data analysis application. The solution collects ambient data in real-time from multiple sensors (temperature, moisture, ammonia (NH3), carbon dioxide (CO2), fine particles, brightness, hydrogen sulfide (H2S), air speed, anemometer, black globe).  Peek’ture pasture monitoring system (Copeeks): Camera-controlled Livestock monitoring. The intelligent monitoring of your rangelands and pastures.  Crodeon Reporter (Crodeon): Real-time monitoring of the atmosphere in livestock building. Reporter is a plug & play IoT sensor device with a reliable wireless internet connection using cellular data.  AIHerd (AIHerd): The system detects early signs of disease at the very first symptom. Cameras detect unusual behavior, variations in feeding or movement, enabling farmers to intervene quickly to ensure animal welfare and prevent the development of disease.  Pigxcel (Smart Agritech Solution of Sweden): The solution weighs pigs automatically with the help of camera technology and AI.  PigInspector (CLK GmbH): Camera-based system with 5 water-resistant cameras and 20 LED lights used to assess ear, tail and skin lesions and tail length in pigs. Others: Tools & Data  FarmLife (Medria): Monitoring system for dairy and suckler cows. The animals are equipped with activity sensors. The services are: heat detection, calving detection, monitoring of thermal stress, activity, feeding behaviors, health and time spend in pasture.  Sneezy Protect (Adventiel): Monitoring system for pigs. AI-based sound analysis for early detection of respiratory disease of pigs.  Soundtalks (Boehringer Ingelheim): Microphone-controlled Livestock monitoring. 24/7 sound data analysed using Artificial Intelligence provides you with a trustworthy and objective way of detecting respiratory problems earlier.  Serket (Serket-tech): Camera-controlled Livestock monitoring. Using regular security cameras and artificial intelligence to identify health and environmental changes early on.  Activity meter system (DeLaval): Activity trackers. The smartest way to help ensure breeding success and uphold a healthy herd. Activity data is presented on screen in an easy-to-read, graphical format.  Porphyrio (Evikon Porphyrio): Smart livestock (poultry) management system using data from the barn, time series analysis, prediction and alerting.  Digit Animal (Digit Animal): Animal GPS tracker, BLE sensors and analytics on the collected data. Control the fattening of your animals.  StickNtrack (Sensolus): Multipurpose GPS tracker system and dashboarding. All-in-one tracking solutions.  INSYLO (INSYLO): The platform simplifies the management of your livestock business and provides tools for real-time management of your farm. Achieve zero waste of feed, reduce expenses and improve animal health.  Weight-Detect TM (PLF Agritech Europe/ Innotech Vision): Estimate the overall weight taking shape measurements in the image. Contactless weighing system. Provides daily report on total pen weight and average animal weight.  iDOL 65 camera (Dol sensors): Solution for measuring pig weight with the use of 3D technology. All based of thousands of automated digital imaging weights per day.  STREMODO (FBN): Recognition of the stress screams of domestic pigs. 101100622/agrifoodTEF AGRIFOOD TEF 32  PigInspector (CLK GmbH): Camera-based system with 5 water-resistant cameras and 20 LED lights used to assess ear, tail and skin lesions and tail length in pigs.  Bleeding Control (CLK GmbH): Exact determination of the amount of blood drained after stunning in pigs.  ChickenCheck Footpad camera (CLK GmbH): Camera system & AI for automatic detection of footpad dermatitis and lesions in chickens.  ChickenCheck Hockburn camera (CLK GmbH): Camera system & AI for automatic detection of hock burns in chickens.  ChickenCheck Catch Damage (CLK GmbH): Camera system & AI for automatic detection of catch damage in chickens.  Tear staining sensor (WEL2BE): Tear staining sensor for pigs.  ChickTrack (FarmWorx): Activity level of poultry. ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity.  BroilerZoom (Animoni): Assess body weight of poultry. The broiler weight is the single most important parameter in broiler production and processing.  Smaxtecclassic Bolus (SmaXtec animal care GmbH): The SmaXtec Classic Bolus SX.2 is a device that continuously measures the inner body temperature, rumination activity, and movement activity of cows. It is placed inside the cow’s reticulum, and the data it collects is wirelessly transmitted to smaXtec read-out devices in real-time.  Smaxtecph Bolus (SmaXtec animal care GmbH): The SmaXtec pH Bolus SX.2 is a device that continuously measures the pH levels, inner body temperature, rumination activity, and movement activity of cows. It is placed inside the cow’s reticulum, and the data it collects is wirelessly transmitted to smaXtec readout devices in real-time.  Smaxtec (Smaxtec Animal Care): The Smaxtec sensor system is an innovative solution for continuous internal health monitoring of cattle. Users can receive data on temperature (also pH values for the research version), crucial for detecting health issues early.  Kuhtracking (Mechatronic Austria GmbH): Kuhtracking, or Cow Tracking, is a technology used in livestock farming, particularly in dairy farming, to monitor animal health and manage herds. It involves the use of sensors and electronic devices to collect data on various parameters related to the cows' behavior and health.  Smartbow (Smartbow GmbH): The system is an advanced dairy cow monitoring technology, a leading animal health company. It uses a proprietary artificial intelligence system called Animal Pattern Recognition IntelLigence (APRIL) to identify and locate animals before they show visible signs of estrus and various health-related behaviors.  PigVision Mobile Sows (AgroVision Poland): The PigVision Sows program makes it easy to enter data on sows and ear-tagged animals. It allows you to access summaries and up-to-date information at any time, providing excellent support for your daily tasks. Sow data is obtained by entering the sow number manually or by scanning a barcode or QR code on the sow card.  Heatime/SenseHub (SCR/Allflex): Heatime systems/ SenseHub offer advanced monitoring for dairy cows' health and reproduction, using activity sensors to detect heat and health events, providing data for actionable insights to improve herd management.  CowControl (Nedap): CowControl provides comprehensive monitoring of cows' health and activity, using accelerometers to analyse behaviour patterns, which helps in detecting heat and health events for better herd management.  MooMonitor (Dairymaster): Is a collar-based system that monitors the behaviour of dairy cows, alerting farmers to signs of estrus and potential health issues through changes in movement and vocalization patterns. 101100622/agrifoodTEF AGRIFOOD TEF 33  CowScout (GEA Farm Technologies): CowScout utilizes motion sensors to track the activity and behaviour of dairy cows, providing farmers with data to optimize herd management through heat and health monitoring.  DeLaval Body Condition Scoring (BCS) System (DeLaval): The system employs advanced computer vision and image analysis technology to automatically assess the body condition of cows. This system captures images of the cows and uses algorithms to analyse specific body points that are critical for determining body condition score.  LiveStock Planner (Hencol): The weight of the animals is automatically collected when E-ID tagged animals walk through a weighing station several times a day. The system provides forecasts of when the animals are ready for slaughter and meatfeed analyses. Pros of technology:  Real-time operation: Ongoing, continuous monitoring and reporting of relevant health parameters of animals, livestock housing, etc. allows to control the health status of animals in real time and react quickly in case of any changes and problems.  Multitasking: A significant part of the indicated devices has the ability control multiple environmental and animal health parameters.  Simplicity of assembly and use: Most of the presented devices and control systems are simple to assemble or install and operate, largely operating unmanned once mounted or installed. Cons of technology:  System costs: In cases of automatic devices for dedicated work: milking parlors, robots for feeding, waste and manure removal, quite high costs of purchasing system components (devices),  Targeted application: Some of the animal health parameter control systems are systemically oriented only for use to the control of a specific type of animal, such as poultry, pigs, cattle. Trends regarding technology:  Cost reduction: Innovation is moving in the direction of reducing component and implementation costs.  Unification of applications: Checking the possibility of adding other types of animals to the device's work resources and adding additional functionalities. Potential agrifoodTEF services and scope:  Animal welfare: Tests to determine the level of animal welfare,  Integration with other applications: check, verify use of test results in other in other animal welfare applications. Tests: 1. Measurement, evaluation of environmental components: Measurements of selected components of the animal's environment (humidity, temperature, etc.) in real conditions of livestock buildings. 2. Measurement, evaluation of animal welfare control devices: Adjustment of parameters to different types of animals, their age, etc., evaluation of the possibility of device operation during the stay of animals outside livestock buildings. 3. Functionality of automatic and autonomous devices: Measurements of the operating parameters of these devices, adjusting their parameters to specific working conditions and animal requirements or conditions, such as those resulting from the construction of livestock buildings or their additional equipment. Validation:  Comparison the data collected with official weather and climate data.  Analysis data collected in different scenarios. 101100622/agrifoodTEF AGRIFOOD TEF 34 6. Future steps and conclusions This Yearly Report serves as a valuable resource for understanding the current landscape and future directions of AI and robotic technologies in agriculture. It highlights the growing importance of digital innovation in shaping sustainable and efficient agricultural practices. The report presents the results of continuous monitoring of the latest available technologies and their use cases, focusing on their usability for testing and experimenting with AI and robotic solutions in the agrifood sectors. Monitoring is done in close cooperation with a network of ecosystem partners, including large technology providers and smaller farms and enterprises. The aim of the catalogue is to present solutions available in commercially available products or tested by AgrifoodTEF in the context of specific agricultural use cases. The overview shows the ranges of possible applications of various application technologies in machinery and implements used in agricultural treatments and technologies, as well as those used inside farms and livestock buildings. This document will be used internally by consortium members to modify the scope of their services to meet the needs of the rapidly changing market for newly developed products. It serves as a baseline document for organizing information on the technologies, their application in agricultural work, their suppliers, and the possibility of their practical application. Future Actions and Initiatives To enhance the catalogue's functionality and ensure its relevance, the consortium members plan to implement several key initiatives. These initiatives will adapt the services offered to prepare for upcoming technological changes and new innovations. The targeted scenarios and initiatives include:  Development of an online service with search functionality: a user-friendly online platform featuring an advanced search engine for the catalogue is planned. This will allow users to efficiently navigate and access the extensive array of information in the catalogue, tailored to their specific needs and queries.  Introduction of a technology submission feature: the platform will include a submission form, enabling both partners at AgrifoodTEF and external entities to contribute information about new technologies. This feature is designed to facilitate the continuous expansion of the catalogue with the latest innovations in agricultural technology.  Creation of an automated web crawler: an automated tool that will regularly scan and extract relevant data from updated databases is planned. This tool will not only keep the catalogue up to date but also identify potential business partners interested in AgrifoodTEF’s research and development services. Additionally, the tool will scan networks to identify subjects of interest among SMEs, farmers, and agricultural organizations related to hi-tech solutions.  Merge existing and potential customer information: it is planned to merge anonymous information obtained during service provision or the onboarding phase from Service Requests Descriptions. This includes potential AgrifoodTEF customers who will be surveyed.  Map technologies utilized by customers of EDIHs: a yearly survey will be issued to gather relevant information on technologies used by customers of EDIHs for agricultural solutions. This step is a natural progression since the cooperation between AgrifoodTEF and EDIHs is based on common information exchange and assisting customers in solution development. To implement these initiatives, AgrifoodTEF members will seek information from EDIH companies on the technologies they are currently using, implementing, or developing. This knowledge will help prepare the technical feasibility of validating these technologies under different conditions and identify opportunities for further collaboration.