1. Overview & History
EdenCore Technologies is a Greek agricultural technology company headquartered in Athens, specialising in artificial intelligence, computer vision and precision crop-management solutions for orchards, vineyards and other high-value agricultural crops.
Founded in 2021, the company originated as a spin-off of the Agricultural University of Athens, combining academic research in precision agriculture with commercial development of digital farming technologies.
The company was co-founded by Nikos Mylonas and Dr. Spyros Fountas, bringing together expertise in computer vision, agricultural engineering, artificial intelligence and agronomic research.
Its original business operated under the name Eden Library, a project focused on agricultural data collection and the development of AI-based analytical solutions.
In January 2025, the company adopted the EdenCore identity, reflecting a strategic transition from an agricultural data platform toward integrated hardware and software solutions for field operations.
This transition was accompanied by the development of Viewer, a tractor- and sprayer-mounted camera system designed to monitor crops, estimate yields, assess vegetation and support variable-rate spraying.
Unlike agricultural robotics companies developing autonomous vehicles, EdenCore concentrates on adding intelligent sensing and decision-making capabilities to existing agricultural machinery.
Its principal customers are growers of high-value perennial crops, where fruit quality, plant health, labour requirements and crop-protection costs strongly influence profitability.
By October 2026, EdenCore had established an international commercial presence and was expanding its activities across Europe, the Americas and Oceania.
2. Corporate Structure & Ownership
EdenCore operates as a privately held Greek agricultural technology company under the legal name EdenCore Technologies Private Company.
Its headquarters and principal business address are located at 84 Leoforos Athinon, Athens, Greece.
The company maintains close technological connections with the Agricultural University of Athens, reflecting its origins in academic research and applied agricultural engineering.
| Corporate information | Details |
|---|---|
| Company | EdenCore Technologies |
| Original identity | Eden Library |
| Founded | 2021 |
| Headquarters | Athens, Greece |
| Legal structure | Private company |
| Co-founders | Nikos Mylonas and Spyros Fountas |
| Chief Executive Officer | Nikos Mylonas |
| Scientific co-founder | Dr. Spyros Fountas |
| Academic origins | Agricultural University of Athens |
| Principal product | Viewer |
| Main business | AI-enabled precision agriculture |
The company has attracted investment from Greek venture-capital firms and agricultural industry participants.
In October 2024, EdenCore announced a ā¬400,000 investment from Uni.Fund II.
A subsequent financing round of ā¬900,000 was announced in December 2025, led by Uni.Fund, with participation from Loggerhead Ventures, agricultural sprayer manufacturer AGROMA and Theti Club angel investors.
The financing was intended to support international commercial expansion and further development of Viewer.
AGROMA’s participation is particularly relevant because EdenCore’s technology is designed to integrate with agricultural sprayers.
The relationship creates potential opportunities for combining AI-based crop sensing with conventional crop-protection machinery.
3. Management & Technical Expertise
EdenCore combines expertise in agricultural engineering, software development, computer vision and business development.
Its founding management and technical team has included:
| Executive | Documented role |
|---|---|
| Nikos Mylonas | Co-founder and CEO |
| Dr. Spyros Fountas | Co-founder and scientific director |
| Giannis Avgoustakis | Chief Technology Officer |
| Giannis Malounas | Chief Operating Officer |
| Giorgos Tsiantis | Finance and business development |
| Georgina Finou | Marketing and product development |
These roles reflect the company’s early organisational structure and should not be interpreted as a fully verified current executive roster.
EdenCore’s scientific background is an important characteristic.
Developing computer-vision systems for agriculture requires more than conventional image recognition. Algorithms must account for differences in crop varieties, growth stages, lighting conditions, vegetation density and field environments.
The company’s research-led origins provide a foundation for developing and validating models adapted to these agricultural conditions.
4. Core Business & Technology Portfolio
EdenCore’s business is centred on its proprietary Viewer platform.
Viewer combines cameras, artificial intelligence, digital mapping and machinery communication systems to support several agricultural operations.
| Technology / service | Principal application |
|---|---|
| Viewer | Tractor-mounted AI crop-scanning platform |
| Precision Spray | Real-time variable-rate crop-protection application |
| Yield Monitoring | Fruit, flower and bud counting |
| Yield Prediction | Pre-harvest production forecasting |
| Vigor Mapping | Tree-level vegetation assessment |
| Disease Mapping | Detection and localisation of visible symptoms |
| Fruit Color Analysis | Assessment of fruit development and colour |
| Web Dashboard | Crop maps, analytics and reporting |
| Sprayer Integration | Communication with compatible spraying equipment |
| Prescription Mapping | Spatially differentiated crop-management instructions |
The company’s commercial proposition is based on combining several applications within a common sensing platform.
This is important because individual precision-agriculture technologies can be difficult to justify economically when they perform only one task.
By supporting crop monitoring, yield prediction and precision spraying, Viewer is designed to generate value across multiple stages of the growing season.
5. Viewer ā AI-Powered Crop Camera System
Viewer is EdenCore’s flagship commercial product.
It is a modular camera-based system designed to be installed on tractors, agricultural sprayers and other suitable carriers.
The system uses high-resolution RGB cameras, active lighting and AI algorithms to collect detailed information about crops while the machine travels through the field.
Its architecture is designed for orchards, vineyards and other crops grown in rows.
Viewer ā Principal technical characteristics
| Characteristic | Description |
|---|---|
| Product | Viewer |
| Machine category | AI crop-scanning and sprayer-control system |
| Camera architecture | Two outward-facing RGB cameras |
| Illumination | Active LED lighting |
| Installation | Tractor or sprayer mounting |
| Crop observation | Both sides of the machine |
| Image processing | Proprietary AI and computer vision |
| Depth information | Stereo-depth estimation through image-processing algorithms |
| Maximum reported crop height | Approximately 4ā5 metres |
| Positioning | GPS-referenced crop observations |
| Connectivity | Data transfer to web dashboard |
| Principal functions | Crop monitoring, yield prediction, precision spraying |
| Sprayer communication | CANOpen, SAE J1939 and ISOBUS-compatible integration |
The two-camera configuration allows Viewer to observe vegetation on both sides of the tractor or sprayer.
Active lighting helps maintain image quality under variable sunlight conditions and during operations with reduced natural illumination.
The system can be mounted on the front linkage, bonnet or other suitable positions, depending on the machine and crop architecture.
Its modular design is intended to accommodate different plant heights and training systems.
Viewer’s principal industrial advantage is that it can be integrated into existing machinery fleets without requiring growers to purchase a dedicated autonomous vehicle.
6. Computer Vision & AI Architecture
Viewer uses artificial intelligence to interpret images collected during field operations.
The system is designed to recognise crop structures and derive information relevant to agricultural management.
Its principal analytical capabilities include:
- detection and counting of buds;
- flower recognition and counting;
- fruit detection and counting;
- estimation of canopy volume;
- assessment of vegetation density;
- identification of visible plant-health anomalies;
- spatial mapping of crop characteristics.
The technology combines image acquisition, onboard processing and subsequent analysis through EdenCore’s digital platform.
GPS coordinates allow observations to be associated with specific locations in the orchard or vineyard.
This creates opportunities for repeated monitoring of individual trees or crop sections throughout the season.
A key distinction is that computer vision observes visible crop characteristics.
It does not directly measure every physiological or biological condition affecting the plant.
For example, visible symptoms may indicate disease or stress, but their precise causes may require agronomic investigation or laboratory confirmation.
EdenCore explicitly positions its technology as a decision-support system that complements professional agronomic expertise.
7. Precision Spray ā Real-Time Variable-Rate Application
Precision spraying is one of Viewerās most commercially important functions.
The system measures vegetation characteristics while the tractor or sprayer moves through the crop.
This information can be communicated to compatible spraying equipment to adjust application rates in real time.
Conventional orchard spraying often applies a relatively uniform dose despite substantial variation in canopy volume and vegetation density.
Viewer is designed to adapt spraying intensity to the vegetation actually present.
Precision Spray ā Documented capabilities
| Function | Specification or application |
|---|---|
| Application strategy | Canopy-based variable-rate spraying |
| Working speed | Up to 10 km/h |
| Minimum reported gap detection | Approximately 20 cm |
| Automatic headland shut-off | Supported |
| Left/right control | Supported |
| Section/nozzle control | Supported on compatible systems |
| PWM control | Supported |
| Maximum controlled channels | Up to 30 |
| Prescription maps | Supported |
| As-applied maps | Supported |
| Manufacturer-reported chemical savings | Up to 40% |
Viewer can operate with different levels of spraying-system sophistication.
Basic configurations support left/right activation and flow adjustment.
More advanced installations can use pulse-width modulation and individual section or nozzle control.
The system supports integration with compatible control units and established agricultural communication protocols.
EdenCore reports chemical-use reductions of up to 40% under suitable operating conditions.
These are company-reported results, not a guaranteed saving for every crop or sprayer.
Actual performance depends on canopy variability, sprayer configuration, operating speed, product requirements and calibration.
The technology also does not eliminate the need for appropriate application rates, coverage assessment or compliance with crop-protection regulations.
8. Yield Monitoring & Fruit Counting
Viewer also provides crop-monitoring capabilities throughout the growing season.
Its computer-vision algorithms can identify and count buds, flowers and fruits.
These measurements support estimates of crop development and potential production.
The system is designed to generate information at both individual-tree and orchard-block level.
Principal yield-monitoring functions
| Function | Management relevance |
|---|---|
| Bud counting | Early assessment of production potential |
| Flower counting | Monitoring flowering intensity |
| Fruit counting | Estimation of crop load |
| Yield forecasting | Harvest planning |
| Spatial yield mapping | Identification of productivity variation |
| Seasonal comparisons | Evaluation of crop development |
| Fruit colour assessment | Monitoring selected maturity characteristics |
EdenCore reports fruit-counting accuracy exceeding 90% in selected applications.
The company also indicates that yield predictions can be generated approximately two to three months before harvest.
These capabilities can support decisions involving labour planning, harvesting logistics, storage requirements and commercial supply commitments.
However, fruit counts and final harvested yield are not identical.
Fruit drop, growth conditions, disease, weather and fruit size can all influence production between the date of scanning and harvest.
Consequently, prediction accuracy should be evaluated by crop, season and operating environment.
9. Vineyard Validation ā Ktima Kir-Yianni
One of EdenCore’s documented field-validation projects involved Ktima Kir-Yianni, a Greek wine producer.
The project evaluated Viewer in vineyards in the Amyndeon region over two growing seasons.
Testing included yield mapping and early production forecasting for Sauvignon Blanc.
The company reported prediction accuracy of 92.5% in 2024 and 98.2% in 2025.
These results are significant because vineyards present complex conditions for visual yield assessment, including variation in canopy density, cluster visibility and fruit development.
The project demonstrates the potential of repeated camera-based monitoring to support vineyard production planning.
Nevertheless, the results refer to a specific field-validation context.
They should not be interpreted as a general guarantee that Viewer will achieve comparable accuracy across all grape varieties, vineyard structures and growing seasons.
The commercial importance lies in the ability to generate production information before harvest rather than relying exclusively on manual sampling.
10. Vigor Mapping & Plant-Health Assessment
Viewer also provides tree-level vegetation analysis.
The system uses images and estimated canopy geometry to evaluate vegetative development, producing spatial information about differences in plant growth.
Unlike conventional satellite vegetation indices, Viewer operates close to the crop and captures detailed images of individual trees or sections of vegetation.
Its principal capabilities include:
- assessment of canopy volume and density;
- estimation of vegetative vigour;
- identification of abnormal growth patterns;
- detection of missing or dead trees;
- mapping of differences between orchard rows;
- monitoring of seasonal vegetation development.
This information can help growers identify areas requiring further investigation.
For example, a section of an orchard displaying weaker growth may be affected by irrigation problems, nutrient limitations, disease or other agronomic constraints.
Viewer can help locate the anomaly, but additional investigation may be necessary to establish its cause.
The technology is particularly suitable for structured orchards and vineyards where crop geometry allows repeated, comparable observations.
11. Disease Recognition & Predictive Crop Protection
EdenCore is developing AI-based recognition of visible crop-health symptoms.
The technology uses high-resolution imagery and trained models to identify potential disease or stress indicators.
Its objective is to support earlier and more targeted agronomic intervention.
However, disease recognition is more complex than identifying fruits or estimating canopy size.
Symptoms can vary between crops, varieties, growth stages and environmental conditions. Different diseases may also produce similar visual characteristics.
EdenCore therefore emphasises the importance of field-specific model training and expert validation.
The system is best understood as an AI-assisted scouting and risk-identification tool, rather than a universal diagnostic instrument.
Its strategic value lies in reducing the time required to locate potentially problematic areas and improving the frequency of crop monitoring.
12. Supported Crops & Agricultural Applications
EdenCore concentrates on high-value crops where visual monitoring can support commercially important decisions.
| Crop | Principal Viewer applications |
|---|---|
| Apples | Fruit counting, yield estimation, canopy assessment |
| Pears | Crop-load monitoring and vegetation mapping |
| Peaches | Fruit counting and yield forecasting |
| Nectarines | Yield monitoring and crop assessment |
| Cherries | Fruit monitoring and orchard intelligence |
| Avocados | Canopy assessment and fruit monitoring |
| Mangoes | Orchard scanning and production intelligence |
| Blueberries | Fruit monitoring and crop assessment |
| Wine grapes | Yield prediction and vineyard mapping |
| Table grapes | Fruit monitoring and vegetation analysis |
The platform is designed for different orchard training systems, with particular suitability for two-dimensional or relatively accessible canopy structures.
Commercial maturity may differ between crops and individual analytical functions.
Support for a crop should not automatically be interpreted as evidence that every disease-recognition or yield-prediction model has achieved the same level of validation.
13. Commercial Deployment & International Markets
EdenCore has moved beyond initial field trials into international commercial deployment.
In December 2025, the company reported Viewer operating in more than ten countries and covering over 3,000 hectares.
By October 2026, industry reporting indicated a larger commercial footprint.
| Commercial indicator | Reported position, October 2026 |
|---|---|
| Viewer units in use | Approximately 80 |
| Countries | Approximately 15 |
| Cumulative scanned area | More than 6,000 hectares |
| Principal regions | Europe, North America, South America and Oceania |
| European purchase-price indication | ā¬15,000āā¬20,000 |
| European annual rental-price indication | ā¬6,000āā¬10,000 |
The figures are company-reported or based on industry interviews and should not be treated as independently audited sales data.
Cumulative hectares scanned should also be distinguished from the number of unique hectares under management, because repeated field passes can contribute to the total.
Viewer is offered through purchase and rental arrangements, with pricing dependent on configuration, geography and distributor.
The company’s international development depends on expanding commercial partnerships and ensuring compatibility with the sprayers and tractors already used in target markets.
14. Partnerships & Agricultural Machinery Integration
EdenCore’s business model depends on collaboration with agricultural equipment manufacturers, technology providers and distribution partners.
Its documented relationships include:
| Organisation | Nature of relationship |
|---|---|
| AGROMA | Agricultural sprayer manufacturer, investor and commercial partner |
| Uni.Fund | Venture-capital investor |
| Loggerhead Ventures | Venture-capital investor |
| Agricultural University of Athens | Academic and technological origins |
| Bayer | Historical field trials and technology evaluation |
| Syngenta | Historical field testing |
| Corteva | Historical AI application development |
| John Deere | Participation in a 2023 technology pilot |
| Agrotehniki | Documented distribution relationship in Bulgaria |
These relationships are not all equivalent.
An investment, research collaboration or technology pilot does not necessarily imply an ongoing commercial integration agreement.
The participation of AGROMA is especially relevant because sprayer manufacturers can integrate crop-sensing technologies into existing equipment architectures.
EdenCore’s support for standard agricultural communication protocols may also facilitate broader OEM integration.
The company’s long-term opportunity lies in becoming a technology supplier to multiple agricultural machinery manufacturers rather than depending exclusively on sales of standalone camera systems.
15. Research, Development & Innovation
EdenCore’s research activities concentrate on computer vision, machine learning and agricultural data analysis.
Its technology development is influenced by the need to operate under changing field conditions.
Important engineering challenges include variable lighting, foliage density, fruit occlusion, crop differences and the computational requirements of real-time image processing.
The company’s development priorities include improving:
- image-recognition accuracy;
- crop-specific AI models;
- depth and canopy estimation;
- yield-prediction reliability;
- real-time sprayer communication;
- disease and anomaly detection;
- digital mapping and reporting;
- installation flexibility.
In October 2025, Viewer received the Fruit Attraction Innovation Hub Award in the Fruit & Vegetable Industry category in Madrid.
The award provided external recognition of the company’s technological development.
Nevertheless, the longer-term commercial significance of Viewer will depend on measurable operating benefits rather than innovation awards alone.
16. Product Scope Clarification
EdenCore develops and commercialises:
- AI-powered agricultural camera systems;
- crop-monitoring hardware;
- computer-vision algorithms;
- fruit, flower and bud counting;
- yield-prediction software;
- tree-level vegetation mapping;
- visible disease-symptom recognition;
- precision-spraying control technology;
- digital orchard and vineyard maps;
- agricultural data dashboards;
- machinery communication and integration software.
EdenCore does not manufacture as a core product category:
- agricultural tractors;
- complete self-propelled sprayers;
- conventional trailed or mounted sprayers;
- autonomous agricultural vehicles;
- fruit-harvesting robots;
- agricultural engines;
- irrigation systems;
- crop-protection chemicals.
Its principal industrial identity is AI-based crop sensing and precision machinery control for high-value crops.
17. Strengths & Competitive Advantages
EdenCore’s competitive position is supported by several characteristics.
- A multifunctional sensing platform.
- Compatibility with existing tractors and sprayers.
- Dual-camera crop observation.
- Active lighting for more consistent image acquisition.
- AI-based fruit counting and yield forecasting.
- Real-time variable-rate spraying capability.
- Tree-level vegetation analysis.
- Applications across several high-value crops.
- Academic research origins.
- Commercial deployment across multiple continents.
- Relationships with agricultural machinery and technology companies.
- A relatively accessible retrofit model compared with purchasing a complete autonomous machine.
Its strongest competitive advantage is the ability to combine crop intelligence and direct machinery control within one platform.
This differentiates Viewer from systems that only collect images or produce maps for later analysis.
18. Weaknesses & Constraints
EdenCore also faces structural challenges associated with emerging agricultural technology businesses.
Its relatively small corporate scale limits the resources available for simultaneous product development, international expansion and customer support.
Computer-vision performance can also vary with crop structure, lighting, weather and image visibility.
Additional constraints include:
- competition from established precision-spraying technology providers;
- the need to validate AI models across many crop varieties;
- dependence on compatible sprayer-control systems;
- varying machinery installation requirements;
- limited independently verified financial information;
- the challenge of demonstrating consistent return on investment;
- the need for reliable international technical support;
- potential differences in disease-recognition performance between crops.
Another important limitation concerns the economic value of crop monitoring.
Detailed maps and fruit counts do not automatically generate savings. Their value depends on whether growers can use the information to improve spraying, harvesting, labour allocation or other decisions.
EdenCore must therefore demonstrate benefits at the level of complete farm operations rather than individual analytical functions.
19. Outlook & Opportunities
EdenCore’s future development is closely connected to the digitalisation of high-value crop production.
Three opportunities are particularly important.
Expansion of precision spraying. Growers face increasing pressure to reduce unnecessary crop-protection inputs while maintaining effective disease and pest management.
Predictive orchard management. More accurate yield forecasts can support labour planning, commercial commitments and harvest logistics.
OEM integration. Agricultural machinery manufacturers may increasingly incorporate AI crop sensing into conventional sprayers and other equipment.
The company’s multifunctional architecture creates an opportunity to spread hardware costs across several applications.
However, its ability to scale will depend on crop-specific validation, distribution capabilities, installation simplicity and the reliability of real-time machinery control.
Summary Table
| Category | Highlights |
|---|---|
| Company | EdenCore Technologies |
| Country | Greece |
| Headquarters | Athens |
| Founded | 2021 |
| Original Identity | Eden Library |
| Rebranding | January 2025 |
| Ownership | Privately held |
| Co-founders | Nikos Mylonas and Spyros Fountas |
| CEO | Nikos Mylonas |
| Academic Origins | Agricultural University of Athens |
| Core Business | AI-powered crop sensing and precision spraying |
| Flagship Product | Viewer |
| Hardware | Dual RGB cameras with active lighting |
| Main Applications | Yield monitoring, precision spraying, vigour and disease mapping |
| Sprayer Integration | CANOpen, SAE J1939 and ISOBUS-compatible systems |
| Reported Chemical Savings | Up to 40% |
| Reported Fruit-Counting Accuracy | Above 90% in selected applications |
| 2024 Investment | ā¬400,000 |
| 2025 Seed Financing | ā¬900,000 |
| Reported Units in Use, 2026 | Approximately 80 |
| Reported Geographic Presence, 2026 | Approximately 15 countries |
| Reported Cumulative Scanned Area | More than 6,000 hectares |
| Principal Markets | Orchards, vineyards and high-value crops |
| Strategic Position | AI Crop Intelligence, Precision Spraying & Yield Prediction |
Bottom Line
EdenCore Technologies represents an emerging category of precision-agriculture companies focused on turning conventional agricultural machinery into intelligent crop-monitoring and application platforms.
Its flagship Viewer system combines computer vision, artificial intelligence and sprayer communication technology to support yield prediction, crop-health assessment and variable-rate spraying.
The company’s competitive proposition is particularly relevant to orchards and vineyards, where variations in canopy structure, crop load and plant health create opportunities for more precise management.
Unlike companies developing complete autonomous machines, EdenCore concentrates on a retrofit architecture that can operate with existing tractors and sprayers.
This approach may reduce adoption barriers while allowing growers to introduce advanced sensing capabilities without replacing their principal equipment.
The company’s international deployment, venture-capital financing and agricultural industry partnerships demonstrate progress beyond the initial development stage.
Nevertheless, future competitiveness will depend on demonstrating consistent agronomic accuracy, measurable operating savings and reliable integration across different machinery platforms.
Strategic Position
Industrial Layer: AI Crop Intelligence, Precision Spraying & Digital Orchard Management
EdenCore occupies an increasingly important position within the agricultural machinery value chain:
Crop Imaging ā AI Interpretation ā Tree-Level Intelligence ā Real-Time Application Control ā Agronomic Decision Support
Its industrial significance lies in connecting two activities that have traditionally been separated: crop observation and machinery operation.
Conventional precision-agriculture systems frequently collect field information that is analysed later and converted into management recommendations.
Viewer is designed to shorten this process by allowing crop observations to influence spraying operations directly.
The system also creates value beyond the immediate spraying task through yield monitoring, vegetation mapping and seasonal crop assessment.
This multifunctional approach is particularly important for high-value crops, where specialised technology investments must be justified across a relatively limited number of hectares.
For agricultural machinery manufacturers, EdenCore illustrates the growing importance of intelligent sensing systems that can be integrated into existing equipment.
For growers, its potential value lies in reducing unnecessary chemical application, improving production forecasts and identifying crop-management problems earlier.
EdenCore’s strategic significance lies in demonstrating how AI-powered crop sensing can transform conventional tractors and sprayers into connected, data-generating machines capable of supporting increasingly precise orchard and vineyard management.
