식물 표현형 분석 시장 : 시장 예측(2026-2032년)

The Plant Phenotyping Market is projected to grow by USD 668.19 million at a CAGR of 13.36% by 2032.

Plant Phenotyping Executive Summary

Plant phenotyping is becoming a core capability for modern crop science, precision agriculture, seed development, and climate-resilient farming. By combining controlled-environment platforms, field-based sensors, imaging systems, robotics, unmanned aerial systems, spectroscopy, and environmental data capture, plant phenotyping enables researchers and agricultural decision-makers to measure observable plant traits such as growth rate, canopy structure, biomass, root architecture, stress response, photosynthetic performance, disease symptoms, and yield-related characteristics. The discipline is increasingly important because global agriculture must improve productivity, resource efficiency, and crop resilience while responding to water scarcity, soil degradation, biodiversity pressure, and more frequent climate extremes.

The strategic value of plant phenotyping lies in its ability to connect genotype, environment, and management practices through measurable evidence. High-throughput plant phenotyping accelerates breeding cycles by helping identify drought tolerance, heat tolerance, nutrient-use efficiency, pest resistance, and quality traits with greater precision than manual scoring alone. In controlled environments, phenotyping supports repeatable experimentation; in open-field conditions, it captures real-world crop performance across variable climates and soils. As agricultural research institutions, seed developers, universities, and public-sector crop programs digitize plant science workflows, demand is rising for interoperable phenotyping infrastructure, AI-enabled image analytics, standardized trait ontologies, and scalable field phenotyping systems.

Transformative Shifts in the Plant Phenotyping Landscape

The plant phenotyping landscape is shifting from manual, labor-intensive observation toward automated, sensor-rich, and data-integrated systems. Traditional phenotyping relied heavily on visual scoring and destructive sampling, which limited scale, repeatability, and temporal resolution. Current systems increasingly use RGB imaging, multispectral and hyperspectral imaging, thermal cameras, LiDAR, chlorophyll fluorescence, root imaging, and environmental sensors to collect continuous trait data across growth stages. This shift is improving the accuracy of crop stress detection, plant architecture analysis, and genotype evaluation.

A major transformation is the movement from greenhouse-centric phenotyping to field phenotyping, where crops are monitored under natural environmental variability. Field-based platforms mounted on drones, tractors, gantries, robots, and stationary sensor networks are enabling non-destructive measurement of canopy temperature, vegetation indices, plant height, leaf area, disease progression, and water stress. Another shift is the integration of phenotyping with genomics, transcriptomics, soil data, weather data, and crop models, creating multidimensional datasets for breeding and agronomy. Open data standards, FAIR data principles, and trait ontology harmonization are also gaining importance as stakeholders seek reproducible analytics and cross-location comparability.

Sustainability goals are further reshaping adoption. Plant phenotyping is increasingly used to support climate-smart agriculture, nitrogen-use efficiency research, irrigation optimization, carbon-related plant trait assessment, and disease-resistant crop development. The landscape is also being influenced by declining sensor costs, advances in edge computing, cloud-based analytics, and greater availability of machine learning tools for image segmentation and trait extraction.

Cumulative Impact of Artificial Intelligence on Plant Phenotyping

Artificial intelligence is reshaping plant phenotyping by converting large volumes of complex image, sensor, and environmental data into actionable biological insights. Computer vision models now support automated detection of plant organs, leaf area, canopy cover, flowering time, fruit counts, disease lesions, lodging, wilting, and nutrient deficiency symptoms. Deep learning has improved performance in tasks where traditional image processing struggled, particularly under variable field conditions involving changing light, occlusion, shadows, soil background, and overlapping leaves.

The cumulative impact of AI extends beyond faster trait measurement. AI-enabled phenotyping supports early stress detection before visible symptoms become severe, allowing researchers and growers to identify drought stress, heat stress, pathogen pressure, and nutrient imbalance with higher temporal resolution. Machine learning models also enable integration of multimodal data, including hyperspectral signatures, thermal profiles, LiDAR-derived structure, weather observations, and genomic markers. This integration strengthens genotype-by-environment analysis and improves the identification of traits associated with resilience and resource-use efficiency.

However, the impact of AI depends on data quality, annotation consistency, model validation, and biological interpretability. Field phenotyping models must be trained across diverse crops, growth stages, geographies, and environmental conditions to reduce bias and improve transferability. Explainable AI, standardized benchmark datasets, edge inference, and human-in-the-loop validation are becoming essential for trusted deployment. As AI matures, plant phenotyping is expected to evolve from descriptive trait measurement toward predictive and prescriptive crop intelligence, supporting breeding decisions, experimental design, and precision crop management without replacing domain expertise.

Key Regional Insights for Plant Phenotyping

Asia-Pacific is a highly active region for plant phenotyping due to the scale of agricultural production, exposure to climate variability, and strong public-sector crop research across rice, wheat, maize, pulses, oilseeds, and horticultural crops. China, India, Japan, South Korea, and Australia are advancing digital agriculture through crop improvement programs, remote sensing research, controlled-environment studies, and high-throughput field phenotyping. The region's priorities include heat tolerance, drought tolerance, salinity tolerance, disease resistance, and yield stability, especially for staple crops that support food security.

Europe emphasizes sustainability-driven phenotyping, crop biodiversity, resource efficiency, and regulatory-aligned research. European programs often focus on reducing chemical inputs, improving nitrogen-use efficiency, supporting climate resilience, and enabling data interoperability across research networks. North America demonstrates strong adoption of advanced plant phenotyping technologies across academic research, breeding programs, precision agriculture, and controlled-environment agriculture. The United States and Canada benefit from robust agricultural research infrastructure, extensive field trial networks, and widespread use of unmanned aerial systems, imaging analytics, and environmental sensing, with demand closely connected to maize, soybean, wheat, canola, specialty crops, and drought-resilient trait development.

Latin America is increasingly adopting phenotyping tools to support tropical agriculture, soybean and maize improvement, sugarcane research, coffee, fruits, and climate adaptation. Brazil and Mexico are important contributors due to their agricultural diversity and research emphasis on productivity under variable rainfall, pest pressure, and soil conditions. Africa's plant phenotyping needs are closely linked to food security, smallholder resilience, drought-tolerant crops, disease resistance, and adaptation to changing rainfall patterns, with the strongest opportunities where tools are affordable, rugged, and suitable for field conditions across diverse agroecological zones.

The Middle East is advancing plant phenotyping in response to arid conditions, water scarcity, salinity, and controlled-environment agriculture needs. Research is strongly oriented toward heat-tolerant crops, water-use efficiency, greenhouse systems, and desert agriculture, making thermal imaging, root-zone monitoring, canopy temperature analysis, and precision irrigation analytics especially relevant for regional food security and resource optimization.

Key Group Insights for Plant Phenotyping

NATO member countries show strong relevance for plant phenotyping through agricultural resilience, climate adaptation, and secure food supply priorities across North America and Europe. Their research ecosystems frequently emphasize field phenotyping, digital crop monitoring, remote sensing, and resilient supply chains for grains, oilseeds, pulses, and specialty crops. G7 countries are characterized by advanced research infrastructure, strong digital agriculture adoption, and emphasis on sustainable productivity, with plant phenotyping supporting breeding efficiency, crop stress diagnostics, controlled-environment research, and resource-use optimization.

The European Union places strong emphasis on sustainable agriculture, biodiversity, low-input farming systems, and climate adaptation. Plant phenotyping in the EU is supported by cross-border research collaboration, open science practices, FAIR data principles, and initiatives that connect phenotypic data with genetic resources and environmental datasets. BRICS countries collectively represent a wide range of crop systems and climate conditions, making plant phenotyping relevant for staple grain improvement, tropical agriculture, dryland farming, and food security. China, India, Brazil, Russia, and South Africa contribute to phenotyping demand through large-scale crop research and the need to improve resilience across diverse agroecological zones.

ASEAN countries are strengthening plant phenotyping capabilities as part of broader efforts to improve rice, maize, cassava, oil palm, fruits, and climate-resilient agriculture. The region faces recurring challenges from flooding, heat, pests, and soil constraints, making phenotyping important for evaluating stress tolerance and productivity traits under humid tropical conditions. Collaborative agricultural research and digital farming initiatives are supporting greater use of field sensors, drone imagery, and crop monitoring tools.

The GCC is prioritizing phenotyping in the context of water scarcity, high temperatures, salinity, and food security strategies. Controlled-environment agriculture, greenhouse systems, hydroponics, and desert farming research create demand for plant monitoring platforms that can track growth performance, transpiration, heat response, root-zone dynamics, and water-use efficiency under extreme arid environments.

Key Country Insights for Plant Phenotyping

The United States leads in advanced plant phenotyping applications across field robotics, drone-based imaging, genomics-enabled breeding, and precision agriculture research, with strong emphasis on maize, soybean, wheat, cotton, and specialty crops. China is investing in high-throughput crop phenotyping to support rice, wheat, maize, soybean, and horticultural crop improvement, integrating imaging technologies, AI analytics, and digital agriculture systems. Germany is notable for engineering-driven phenotyping platforms, sensor development, automation, and crop efficiency research, while Japan combines precision sensing, robotics, controlled-environment research, and crop quality evaluation for high-value agriculture.

India's phenotyping needs are closely tied to food security, heat stress, drought stress, salinity, pulses, rice, wheat, and millets, with field-ready and cost-effective platforms holding particular importance. The United Kingdom emphasizes crop science, controlled-environment experimentation, data integration, and sustainable intensification. France focuses on cereal, oilseed, vineyard-related research, agroecology, and climate resilience. Canada focuses on wheat, canola, pulses, and cold-climate crop resilience, using phenotyping to evaluate stress tolerance, yield stability, and disease resistance.

Italy and Spain apply phenotyping in cereals, horticulture, olives, grapes, and Mediterranean crops, with water stress and heat tolerance as major themes. Australia emphasizes drought resilience, heat tolerance, dryland cropping, wheat, barley, pulses, and field phenotyping under highly variable rainfall. Brazil is a major center for phenotyping in soybean, maize, sugarcane, coffee, and tropical crop systems, with strong relevance for heat tolerance, pest resistance, and productivity across diverse biomes. South Korea is advancing smart farming, greenhouse monitoring, crop imaging, and digital breeding applications for high-value crops and controlled-environment systems.

Mexico's priorities include maize diversity, wheat improvement, drought adaptation, and crop performance under water-limited environments, supported by agricultural research networks and field-based evaluation. Russia's large agricultural land base makes phenotyping relevant for wheat, barley, oilseeds, and adaptation to continental climate variability, including cold stress, drought, and season-length constraints.

Actionable Recommendations for Plant Phenotyping Leaders

Industry leaders should prioritize scalable, interoperable, and biologically validated phenotyping solutions that address real research and agricultural decision-making needs. The most effective strategies include designing systems that function reliably across controlled environments and field conditions, integrating multimodal sensors, and ensuring that data pipelines can handle image processing, metadata capture, environmental context, and trait extraction at scale. Solutions should support standardized data formats, trait ontologies, and compatibility with breeding, laboratory, farm management, and geospatial platforms.

AI investment should focus on model robustness, explainability, and transferability across crops, geographies, lighting conditions, and growth stages. Leaders should build curated training datasets, implement rigorous validation protocols, and incorporate expert review into model development. For field phenotyping, emphasis should be placed on rugged hardware, repeatable calibration, georeferenced data capture, and workflows that reduce manual annotation burden. For controlled environments, integration with lighting, irrigation, nutrient delivery, and climate control systems can strengthen experimental reproducibility.

Organizations should also align product development with sustainability and food security priorities, including drought resilience, nitrogen-use efficiency, disease resistance, water-use efficiency, and climate adaptation. Partnerships with agricultural universities, public research programs, seed developers, and crop institutes can accelerate validation and adoption. To improve accessibility, leaders should offer modular platforms, flexible analytics, training support, and service models suitable for both advanced research facilities and resource-constrained field programs.

Research Methodology for Plant Phenotyping Analysis

This executive summary is developed through a structured secondary research methodology focused on verified and data-backed evidence from credible agricultural science, plant biology, remote sensing, and digital agriculture sources. The methodology emphasizes peer-reviewed literature, public agricultural research publications, government and intergovernmental resources, crop science reports, technology documentation, and field phenotyping best-practice references. Insights are synthesized to identify technology trends, regional patterns, application priorities, and strategic implications without relying on market sizing, market share, or forecasting.

The research approach includes thematic analysis of plant phenotyping technologies such as imaging systems, spectroscopy, thermal sensing, LiDAR, robotics, drones, controlled-environment platforms, root phenotyping, and AI-based analytics. It also examines application areas including breeding, stress physiology, disease detection, nutrient efficiency, irrigation management, crop modeling, and climate resilience. Regional and country-level insights are assessed by considering agricultural priorities, crop systems, research infrastructure, climate pressures, and digital agriculture adoption.

Quality control is based on source triangulation, consistency checks across scientific and institutional references, and exclusion of unsupported claims. The methodology avoids promotional assumptions and focuses on evidence-based interpretation of how plant phenotyping is being applied in research, breeding, and agricultural innovation. This ensures that the summary remains useful for executives, R&D leaders, policy stakeholders, technology developers, and agricultural decision-makers seeking a reliable view of the plant phenotyping landscape.

Conclusion

Plant phenotyping is advancing from a specialized research function into a strategic pillar of digital agriculture, crop improvement, and climate-resilient food systems. By enabling precise, repeatable, and non-destructive measurement of plant traits, phenotyping helps bridge the gap between genetic potential and real-world crop performance. Its importance is rising as agriculture faces the combined pressures of climate change, resource scarcity, pest and disease threats, and the need for sustainable productivity.

The strongest momentum is occurring where high-throughput imaging, field robotics, remote sensing, environmental monitoring, and AI analytics are integrated into breeding and agronomy workflows. Regional priorities differ, but the underlying direction is consistent: stakeholders need better tools to understand plant response to stress, improve resource-use efficiency, and accelerate the development of resilient crop varieties. Asia-Pacific, Europe, North America, Latin America, Africa, and the Middle East each bring distinct crop systems and climate challenges that shape phenotyping adoption.

For industry leaders, success will depend on delivering reliable, interoperable, and validated solutions that generate actionable biological insights rather than raw data alone. The future of plant phenotyping will be defined by scalable field deployment, trusted AI, standardized data ecosystems, and alignment with food security and sustainability objectives.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. New Revenue Opportunities
• 3.5. Next-Generation Business Models
• 3.6. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Plant Phenotyping Market, by Offering

• 7.1. Introduction
• 7.2. Hardware
  • 7.2.1. Imaging Systems
    • 7.2.1.1. RGB Cameras
    • 7.2.1.2. Multispectral Cameras
    • 7.2.1.3. Hyperspectral Cameras
    • 7.2.1.4. Thermal Cameras
    • 7.2.1.5. Fluorescence Cameras
    • 7.2.1.6. LiDAR & 3D Sensors
  • 7.2.2. Sensor Systems
    • 7.2.2.1. Environmental Sensors
    • 7.2.2.2. Soil & Root Sensors
    • 7.2.2.3. Gas Exchange & Physiology Sensors
  • 7.2.3. Phenotyping Platforms
    • 7.2.3.1. Ground Platforms
    • 7.2.3.1.1. Wheeled Rovers
    • 7.2.3.1.2. Tracked Vehicles
    • 7.2.3.2. Aerial Platforms
    • 7.2.3.2.1. UAV & Drones
    • 7.2.3.2.2. Manned Aircraft
    • 7.2.3.3. Fixed Installations
    • 7.2.3.3.1. Gantry Systems
    • 7.2.3.3.2. Conveyor & Greenhouse Lines
    • 7.2.3.3.3. Field Towers
• 7.3. Software
  • 7.3.1. Image Analysis
  • 7.3.2. Data Management
  • 7.3.3. AI & Machine Learning Analytics
  • 7.3.4. Experiment Workflow Software
• 7.4. Services
  • 7.4.1. Phenotyping As A Service
  • 7.4.2. Implementation Services
  • 7.4.3. Professional Services
  • 7.4.4. Support Services

8. Plant Phenotyping Market, by Phenotyping Environment

• 8.1. Introduction
• 8.2. Controlled Environment
  • 8.2.1. Greenhouse
    • 8.2.1.1. Bench Scale
    • 8.2.1.2. Commercial Scale
  • 8.2.2. Growth Chambers
    • 8.2.2.1. Single Plant Chambers
    • 8.2.2.2. Walk-In Chambers
  • 8.2.3. Indoor & Vertical Farms
    • 8.2.3.1. Hydroponic Systems
    • 8.2.3.2. Aeroponic Systems
    • 8.2.3.3. Soil & Substrate Systems
• 8.3. Field
  • 8.3.1. Open Field Trials
  • 8.3.2. Protected Field Structures
  • 8.3.3. High Stress Managed Environments

9. Plant Phenotyping Market, by Throughput Classes

• 9.1. Introduction
• 9.2. Manual
• 9.3. Semi Automated
• 9.4. Fully Automated

10. Plant Phenotyping Market, by Trait Category

• 10.1. Introduction
• 10.2. Morphological Traits
  • 10.2.1. Plant Height
  • 10.2.2. Leaf Area & Canopy Cover
  • 10.2.3. Root Architecture
• 10.3. Physiological Traits
  • 10.3.1. Photosynthetic Efficiency
  • 10.3.2. Transpiration & Stomatal Conductance
  • 10.3.3. Water Use Efficiency
• 10.4. Biochemical Traits
  • 10.4.1. Pigments & Chlorophyll
  • 10.4.2. Metabolites
  • 10.4.3. Protein & Oil Content
• 10.5. Stress Response Traits
  • 10.5.1. Abiotic Stress
  • 10.5.2. Biotic Stress
• 10.6. Phenology & Yield Traits
  • 10.6.1. Emergence & Establishment
  • 10.6.2. Flowering Time
  • 10.6.3. Maturity Timing

11. Plant Phenotyping Market, by Crop Category

• 11.1. Introduction
• 11.2. Oilseeds & Pulses
  • 11.2.1. Soybean
  • 11.2.2. Canola & Rapeseed
  • 11.2.3. Sunflower
  • 11.2.4. Pea Lentil & Chickpea
• 11.3. Cereals & Grains
  • 11.3.1. Wheat
  • 11.3.2. Rice
  • 11.3.3. Maize & Corn
  • 11.3.4. Barley
  • 11.3.5. Sorghum
• 11.4. Fruits & Vegetables
  • 11.4.1. Solanaceae
  • 11.4.2. Fruits
  • 11.4.3. Leafy Greens
  • 11.4.4. Cucurbits
• 11.5. Specialty Crops
  • 11.5.1. Ornamentals
  • 11.5.2. Turfgrass
  • 11.5.3. Forest Trees & Perennials
• 11.6. Industrial & Energy Crops
  • 11.6.1. Fiber Crops
    • 11.6.1.1. Cotton
    • 11.6.1.2. Flax & Hemp
  • 11.6.2. Sugar Crops
    • 11.6.2.1. Sugarcane
    • 11.6.2.2. Sugar Beet
  • 11.6.3. Bioenergy Crops

12. Plant Phenotyping Market, by Application Area

• 12.1. Introduction
• 12.2. Biological Research
  • 12.2.1. Functional Genomics
  • 12.2.2. Gene Editing Validation
  • 12.2.3. Systems Biology & Modeling
• 12.3. Agrochemical Development
• 12.4. Plant Breeding
  • 12.4.1. Trait Discovery & Validation
  • 12.4.2. Genomic Selection Support
  • 12.4.3. Variety & Hybrid Trials
  • 12.4.4. Speed Breeding Enablement
• 12.5. Crop Management Research
  • 12.5.1. Irrigation Optimization
  • 12.5.2. Fertilizer Optimization
  • 12.5.3. Integrated Pest Management Research
  • 12.5.4. Canopy & Microclimate Studies
• 12.6. Precision Agriculture Enablement

13. Plant Phenotyping Market, by End User Type

• 13.1. Introduction
• 13.2. Academic & Research Institutes
• 13.3. Seed & Breeding Companies
• 13.4. Agrochemical & Biotech Companies
• 13.5. Contract Research Organizations
• 13.6. Growers & Service Providers

14. Plant Phenotyping Market, by Region

• 14.1. Asia-Pacific
• 14.2. Europe
• 14.3. North America
• 14.4. Latin America
• 14.5. Africa
• 14.6. Middle East

15. Plant Phenotyping Market, by Group

• 15.1. NATO
• 15.2. G7
• 15.3. European Union
• 15.4. BRICS
• 15.5. ASEAN
• 15.6. GCC

16. Plant Phenotyping Market, by Country

• 16.1. United States
• 16.2. China
• 16.3. Germany
• 16.4. Japan
• 16.5. India
• 16.6. United Kingdom
• 16.7. France
• 16.8. Canada
• 16.9. Italy
• 16.10. Australia
• 16.11. Brazil
• 16.12. South Korea
• 16.13. Mexico
• 16.14. Russia
• 16.15. Spain

17. Competitive Landscape

• 17.1. Market Share Analysis, 2025
• 17.2. FPNV Positioning Matrix, 2025
• 17.3. Market Concentration Analysis, 2025
  • 17.3.1. Concentration Ratio (CR)
  • 17.3.2. Herfindahl Hirschman Index (HHI)
• 17.4. Recent Developments & Impact Analysis, 2025
• 17.5. Product Portfolio Analysis, 2025
• 17.6. Benchmarking Analysis, 2025

18. Company Profiles

• 18.1. Nynomic AG
• 18.2. Qubit Systems Inc.
• 18.3. Staphyt
• 18.4. CD Genomics
• 18.5. PSI (Photon Systems Instruments) spol. s r.o.
• 18.6. PhenoVation B.V.
• 18.7. Alteia SAS
• 18.8. SeedX Technologies Inc.
• 18.9. Delta-T Devices Ltd.
• 18.10. Agrela Ecosystems, Inc.
• 18.11. BASF SE
• 18.12. BEX Co., Ltd.
• 18.13. Forschungszentrum Julich GmbH
• 18.14. Gardin Ltd.
• 18.15. Heinz Walz GmbH
• 18.16. HIPHEN
• 18.17. KeyGene N.V.
• 18.18. LGC Limited
• 18.19. LI-COR, Inc.
• 18.20. MaxWell Biosystems AG
• 18.21. METER Group, Inc.
• 18.22. phenoLytics
• 18.23. Phenome Networks Ltd.
• 18.24. Phenospex B.V.
• 18.25. Phenotype Screening Corporation
• 18.26. Plant-DiTech Ltd.
• 18.27. Rothamsted Research Limited
• 18.28. Vienna BioCenter
• 18.29. WIWAM - SMO BV
• 18.30. WPS B.V.