Fruit Picking Harvesting Robots Market by Fruit Type (Apple, Berry, Citrus), Deployment (Mobile, Stationary), Technology, Application, End User, Autonomy, Offering - Global Forecast 2026-2032
Description
The Fruit Picking Harvesting Robots Market was valued at USD 495.81 million in 2025 and is projected to grow to USD 560.05 million in 2026, with a CAGR of 13.21%, reaching USD 1,182.23 million by 2032.
Comprehensive introduction to how robotics convergence with sensing and AI is reframing orchard operations labor models and postharvest quality systems
The convergence of robotics, advanced sensing, and machine intelligence has moved fruit harvesting from experimental pilots toward structured commercial deployment. Growers, integrators, and equipment providers now evaluate harvesting technologies not as isolated tools but as components in broader operational transformations that address labor scarcity, quality consistency, and traceability. Early adopters have demonstrated meaningful gains in repetitive-task performance and injury risk reduction, while researchers continue to refine soft-grasp end effectors and fruit-friendly handling to protect delicate produce.
As the ecosystem matures, interoperability and modularity have emerged as differentiators. Vendors are prioritizing adaptable hardware architectures and software abstraction layers to support a wider range of fruit types and canopy architectures. Consequently, decision-makers should treat robotics as platform investments that require ecosystem alignment across agronomy, systems integration, and supply chain partners. In this context, understanding the technological capabilities, deployment models, and operational trade-offs becomes essential for strategic planning and capital allocation.
Analysis of pivotal technological and deployment transformations driving higher perception fidelity and adaptive manipulation across diverse orchard environments
Recent transformative shifts in the agricultural robotics landscape center on the integration of advanced perception systems and force-aware manipulation, enabling machines to navigate unstructured environments and handle soft fruit with reduced damage. Vision-guided robots that combine two-dimensional and three-dimensional sensing now offer higher fruit-detection reliability and more precise approach vectors, which in turn reduce bruising rates and increase picker throughput when compared to earlier generation systems. Simultaneously, improvements in force control algorithms permit adaptive gripping strategies that account for fruit variability across cultivars and ripeness stages.
Beyond technical advances, deployment models have evolved: mobile platforms capable of navigating row crops coexist with stationary workstations designed for high-throughput sorting and packing. This duality allows growers to select solutions that align with field topology, labor availability, and crop economics. In addition, the proliferation of software tools for fleet coordination and remote monitoring is pushing robotics from single-point automation toward integrated farm management solutions, creating new opportunities for service providers to offer recurring revenue models through maintenance, training, and performance optimization.
Insightful assessment of how recent tariff adjustments reshaped supply chain sourcing, procurement risk management, and vendor selection for harvesting robotics
Tariff policy shifts and trade measures implemented through 2025 have altered procurement dynamics for agricultural machinery and componentry, affecting supply chain resilience and total landed cost considerations for robotics deployments. Increased duties on certain imported components have incentivized some manufacturers to reassess sourcing strategies, localizing key subassemblies where feasible and fostering new partnerships with regional suppliers. This reorganization of supply chains has influenced lead times, warranty support arrangements, and vendor selection criteria for growers considering robotic investments.
At the operational level, companies with diversified production footprints mitigated tariff impacts more effectively than firms reliant on single-country sourcing. As a result, procurement teams now weigh geopolitical risk as a material factor when evaluating capital equipment, preferring vendors with demonstrated redundancy and transparent supply-chain traceability. Consequently, financial planning for automation projects increasingly incorporates scenario modeling for duty fluctuations, logistics constraints, and potential import bottlenecks, which has elevated the importance of contractual flexibility and local service networks.
Deep segmentation perspective connecting crop characteristics, deployment choices, and technology stacks to practical application pathways and buyer expectations
Segmentation insight illuminates where technical capability maps to agricultural need and commercial viability. Based on fruit type, solutions must be tailored to the mechanical properties and canopy structures of Apple, Berry, Citrus, Grape, and Stone Fruit, since each category imposes distinct detection, gripping, and path-planning requirements. Based on deployment, the choice between mobile and stationary platforms depends on field layout, row spacing, and the desired balance between in-field harvesting and centralized processing. Based on technology, the realization of industry goals relies on the interplay among AI machine learning for decision logic, force control for safe handling, sensor integration for environmental awareness, and vision guidance-which further bifurcates into 2D vision and 3D vision modalities-to achieve reliable detection and localization.
Based on application, vendor roadmaps diverge across harvesting, pruning, sorting, and thinning use cases, with systems optimized for one task often requiring adaptation or accessory modules to perform others. Based on end user, differing expectations from farms, orchards, and vineyards influence product roadmaps, service models, and training requirements. Based on autonomy, offerings classified as autonomous or semi autonomous present trade-offs between operator oversight and continuous uptime. Based on offering, the market encompasses hardware, services, and software, where services further include consulting, maintenance, and training; success frequently hinges on bundled services that reduce integration friction and accelerate operational proficiency.
Comprehensive regional analysis linking operational priorities and service networks to the differing adoption patterns observed across world agricultural regions
Regional dynamics determine adoption velocity and the relative emphasis on particular technological features. In the Americas, large-scale specialty crop operations and high labor costs drive interest in fleet-scale mobile solutions and turnkey service agreements, while a strong logistics infrastructure supports integration of robotics into existing packing and cold-chain workflows. In Europe, Middle East & Africa, regulatory frameworks, farm size heterogeneity, and labor policy variations create demand for adaptable platforms that can serve both smallholder orchards and industrial-scale growers, with an emphasis on precision sensing and energy-efficient designs. In the Asia-Pacific region, intense cropping density, diverse fruit varieties, and rapidly modernizing supply chains favor compact, agile systems that can operate in constrained environments and integrate with automated postharvest facilities.
Across regions, local regulatory landscapes and labor programs influence the design priorities of vendors, who must align product durability, service models, and training offerings to regional agronomic practices. Furthermore, aftersales support networks and localized maintenance capabilities often determine long-term user satisfaction more than initial hardware performance, prompting manufacturers to invest in regional partnerships and technical upskilling initiatives to gain traction in target geographies.
Key company dynamics and competitive differentiators that determine vendor adoption, retention, and the formation of robust service-led business models
Competitive dynamics reveal a mix of legacy agricultural equipment firms expanding into mechatronics and specialty robotics startups focused on niche task automation. Leading companies differentiate through depth of integration, robustness of vision and force-control stacks, and the ability to provide end-to-end solutions that include installation, operator training, and field service. Strategic partnerships with component suppliers and local distributors frequently enable faster market access and stronger uptime commitments, which are critical selling points for conservative farm operators.
Product innovation efforts concentrate on modular end effectors, improved gripper compliance, and software toolchains that reduce the time required for crop-specific calibration. Companies that invest in comprehensive training programs and transparent performance metrics tend to secure longer customer lifecycles. In addition, organizations that adopt open integration standards and provide clear APIs for fleet management increase the likelihood of adoption by integrators and third-party software vendors, thereby expanding the addressable use cases and creating network effects that benefit platform incumbents.
Actionable executive recommendations for technology selection, deployment scaling, workforce enablement, and resilient procurement strategies to maximize automation value
Industry leaders should adopt a pragmatic roadmap that balances near-term operational gains with long-term platform scalability. Prioritize pilot deployments that pair the right end effector and perception stack to the primary fruit type, then scale through phased rollouts that validate throughput, fruit quality preservation, and maintenance cadence. Simultaneously, invest in local technician training programs and performance-based service agreements to ensure uptime and reduce end-user friction. Collaboration with growers during early trials accelerates iterative refinement and builds the practical credibility necessary for broader adoption.
Leaders must also develop flexible procurement and financing options to address capital constraints and provide clear total cost of ownership narratives. Where tariffs or component constraints elevate supply risk, cultivating regional supplier relationships and modular designs that permit substitution of critical parts can reduce exposure. Finally, establishing data governance practices and interoperability standards will unlock downstream value through predictive maintenance, yield analytics, and integration with broader farm-management platforms, creating differentiated service offerings that extend beyond the hardware sale.
Transparent research methodology detailing how primary interviews, hands-on assessments, and triangulated secondary sources were used to validate field performance and product claims
The research underpinning these insights combines qualitative expert interviews, technology capability assessments, and structured analysis of adoption case studies to create an integrated view of the sector. Primary inputs included interviews with growers, integrators, and engineering leads; technology roadmaps were assessed through hands-on demonstrations and vendor-provided technical documentation. Secondary analysis encompassed a synthesis of public technical papers, patent filings, and regulatory guidance relevant to agricultural robotics, ensuring that product claims were cross-validated against observed field performance.
Data integrity was maintained through triangulation across sources and by documenting use-case constraints, such as crop variability and canopy geometry, which materially affect system performance. The methodology prioritized transparency around assumptions and limitations, and where gaps in publicly available information existed, follow-up interviews were conducted to clarify operational metrics, maintenance practices, and service level expectations. This mixed-method approach ensures that conclusions reflect both laboratory capability and real-world operational contexts.
Conclusive synthesis emphasizing the centrality of perception, adaptive handling, and service ecosystems in translating robotics pilots into commercially sustainable agricultural operations
As the industry progresses, practical considerations such as end-user training, service ecosystems, and modular design will increasingly determine which technologies gain lasting traction. The interplay between perception fidelity and compliant manipulation will remain central to expanding the range of fruit types that robots can reliably handle, while software-driven orchestration will enable higher utilization through coordinated fleet operations and predictive maintenance. In parallel, vendors that embed strong service propositions and local support networks will reduce barriers to adoption and accelerate replacement of manual harvesting for targeted crops.
Looking ahead, the cumulative effect of improved sensing, adaptive control, and service-led business models suggests a transition from isolated pilots to commercially sustainable operations in contexts where labor economics, crop value, and logistical integration align. Stakeholders who proactively address supply chain risks, invest in human capital, and pursue modular architectures will be better positioned to capture the operational and quality benefits that these systems can deliver.
Note: PDF & Excel + Online Access - 1 Year
Comprehensive introduction to how robotics convergence with sensing and AI is reframing orchard operations labor models and postharvest quality systems
The convergence of robotics, advanced sensing, and machine intelligence has moved fruit harvesting from experimental pilots toward structured commercial deployment. Growers, integrators, and equipment providers now evaluate harvesting technologies not as isolated tools but as components in broader operational transformations that address labor scarcity, quality consistency, and traceability. Early adopters have demonstrated meaningful gains in repetitive-task performance and injury risk reduction, while researchers continue to refine soft-grasp end effectors and fruit-friendly handling to protect delicate produce.
As the ecosystem matures, interoperability and modularity have emerged as differentiators. Vendors are prioritizing adaptable hardware architectures and software abstraction layers to support a wider range of fruit types and canopy architectures. Consequently, decision-makers should treat robotics as platform investments that require ecosystem alignment across agronomy, systems integration, and supply chain partners. In this context, understanding the technological capabilities, deployment models, and operational trade-offs becomes essential for strategic planning and capital allocation.
Analysis of pivotal technological and deployment transformations driving higher perception fidelity and adaptive manipulation across diverse orchard environments
Recent transformative shifts in the agricultural robotics landscape center on the integration of advanced perception systems and force-aware manipulation, enabling machines to navigate unstructured environments and handle soft fruit with reduced damage. Vision-guided robots that combine two-dimensional and three-dimensional sensing now offer higher fruit-detection reliability and more precise approach vectors, which in turn reduce bruising rates and increase picker throughput when compared to earlier generation systems. Simultaneously, improvements in force control algorithms permit adaptive gripping strategies that account for fruit variability across cultivars and ripeness stages.
Beyond technical advances, deployment models have evolved: mobile platforms capable of navigating row crops coexist with stationary workstations designed for high-throughput sorting and packing. This duality allows growers to select solutions that align with field topology, labor availability, and crop economics. In addition, the proliferation of software tools for fleet coordination and remote monitoring is pushing robotics from single-point automation toward integrated farm management solutions, creating new opportunities for service providers to offer recurring revenue models through maintenance, training, and performance optimization.
Insightful assessment of how recent tariff adjustments reshaped supply chain sourcing, procurement risk management, and vendor selection for harvesting robotics
Tariff policy shifts and trade measures implemented through 2025 have altered procurement dynamics for agricultural machinery and componentry, affecting supply chain resilience and total landed cost considerations for robotics deployments. Increased duties on certain imported components have incentivized some manufacturers to reassess sourcing strategies, localizing key subassemblies where feasible and fostering new partnerships with regional suppliers. This reorganization of supply chains has influenced lead times, warranty support arrangements, and vendor selection criteria for growers considering robotic investments.
At the operational level, companies with diversified production footprints mitigated tariff impacts more effectively than firms reliant on single-country sourcing. As a result, procurement teams now weigh geopolitical risk as a material factor when evaluating capital equipment, preferring vendors with demonstrated redundancy and transparent supply-chain traceability. Consequently, financial planning for automation projects increasingly incorporates scenario modeling for duty fluctuations, logistics constraints, and potential import bottlenecks, which has elevated the importance of contractual flexibility and local service networks.
Deep segmentation perspective connecting crop characteristics, deployment choices, and technology stacks to practical application pathways and buyer expectations
Segmentation insight illuminates where technical capability maps to agricultural need and commercial viability. Based on fruit type, solutions must be tailored to the mechanical properties and canopy structures of Apple, Berry, Citrus, Grape, and Stone Fruit, since each category imposes distinct detection, gripping, and path-planning requirements. Based on deployment, the choice between mobile and stationary platforms depends on field layout, row spacing, and the desired balance between in-field harvesting and centralized processing. Based on technology, the realization of industry goals relies on the interplay among AI machine learning for decision logic, force control for safe handling, sensor integration for environmental awareness, and vision guidance-which further bifurcates into 2D vision and 3D vision modalities-to achieve reliable detection and localization.
Based on application, vendor roadmaps diverge across harvesting, pruning, sorting, and thinning use cases, with systems optimized for one task often requiring adaptation or accessory modules to perform others. Based on end user, differing expectations from farms, orchards, and vineyards influence product roadmaps, service models, and training requirements. Based on autonomy, offerings classified as autonomous or semi autonomous present trade-offs between operator oversight and continuous uptime. Based on offering, the market encompasses hardware, services, and software, where services further include consulting, maintenance, and training; success frequently hinges on bundled services that reduce integration friction and accelerate operational proficiency.
Comprehensive regional analysis linking operational priorities and service networks to the differing adoption patterns observed across world agricultural regions
Regional dynamics determine adoption velocity and the relative emphasis on particular technological features. In the Americas, large-scale specialty crop operations and high labor costs drive interest in fleet-scale mobile solutions and turnkey service agreements, while a strong logistics infrastructure supports integration of robotics into existing packing and cold-chain workflows. In Europe, Middle East & Africa, regulatory frameworks, farm size heterogeneity, and labor policy variations create demand for adaptable platforms that can serve both smallholder orchards and industrial-scale growers, with an emphasis on precision sensing and energy-efficient designs. In the Asia-Pacific region, intense cropping density, diverse fruit varieties, and rapidly modernizing supply chains favor compact, agile systems that can operate in constrained environments and integrate with automated postharvest facilities.
Across regions, local regulatory landscapes and labor programs influence the design priorities of vendors, who must align product durability, service models, and training offerings to regional agronomic practices. Furthermore, aftersales support networks and localized maintenance capabilities often determine long-term user satisfaction more than initial hardware performance, prompting manufacturers to invest in regional partnerships and technical upskilling initiatives to gain traction in target geographies.
Key company dynamics and competitive differentiators that determine vendor adoption, retention, and the formation of robust service-led business models
Competitive dynamics reveal a mix of legacy agricultural equipment firms expanding into mechatronics and specialty robotics startups focused on niche task automation. Leading companies differentiate through depth of integration, robustness of vision and force-control stacks, and the ability to provide end-to-end solutions that include installation, operator training, and field service. Strategic partnerships with component suppliers and local distributors frequently enable faster market access and stronger uptime commitments, which are critical selling points for conservative farm operators.
Product innovation efforts concentrate on modular end effectors, improved gripper compliance, and software toolchains that reduce the time required for crop-specific calibration. Companies that invest in comprehensive training programs and transparent performance metrics tend to secure longer customer lifecycles. In addition, organizations that adopt open integration standards and provide clear APIs for fleet management increase the likelihood of adoption by integrators and third-party software vendors, thereby expanding the addressable use cases and creating network effects that benefit platform incumbents.
Actionable executive recommendations for technology selection, deployment scaling, workforce enablement, and resilient procurement strategies to maximize automation value
Industry leaders should adopt a pragmatic roadmap that balances near-term operational gains with long-term platform scalability. Prioritize pilot deployments that pair the right end effector and perception stack to the primary fruit type, then scale through phased rollouts that validate throughput, fruit quality preservation, and maintenance cadence. Simultaneously, invest in local technician training programs and performance-based service agreements to ensure uptime and reduce end-user friction. Collaboration with growers during early trials accelerates iterative refinement and builds the practical credibility necessary for broader adoption.
Leaders must also develop flexible procurement and financing options to address capital constraints and provide clear total cost of ownership narratives. Where tariffs or component constraints elevate supply risk, cultivating regional supplier relationships and modular designs that permit substitution of critical parts can reduce exposure. Finally, establishing data governance practices and interoperability standards will unlock downstream value through predictive maintenance, yield analytics, and integration with broader farm-management platforms, creating differentiated service offerings that extend beyond the hardware sale.
Transparent research methodology detailing how primary interviews, hands-on assessments, and triangulated secondary sources were used to validate field performance and product claims
The research underpinning these insights combines qualitative expert interviews, technology capability assessments, and structured analysis of adoption case studies to create an integrated view of the sector. Primary inputs included interviews with growers, integrators, and engineering leads; technology roadmaps were assessed through hands-on demonstrations and vendor-provided technical documentation. Secondary analysis encompassed a synthesis of public technical papers, patent filings, and regulatory guidance relevant to agricultural robotics, ensuring that product claims were cross-validated against observed field performance.
Data integrity was maintained through triangulation across sources and by documenting use-case constraints, such as crop variability and canopy geometry, which materially affect system performance. The methodology prioritized transparency around assumptions and limitations, and where gaps in publicly available information existed, follow-up interviews were conducted to clarify operational metrics, maintenance practices, and service level expectations. This mixed-method approach ensures that conclusions reflect both laboratory capability and real-world operational contexts.
Conclusive synthesis emphasizing the centrality of perception, adaptive handling, and service ecosystems in translating robotics pilots into commercially sustainable agricultural operations
As the industry progresses, practical considerations such as end-user training, service ecosystems, and modular design will increasingly determine which technologies gain lasting traction. The interplay between perception fidelity and compliant manipulation will remain central to expanding the range of fruit types that robots can reliably handle, while software-driven orchestration will enable higher utilization through coordinated fleet operations and predictive maintenance. In parallel, vendors that embed strong service propositions and local support networks will reduce barriers to adoption and accelerate replacement of manual harvesting for targeted crops.
Looking ahead, the cumulative effect of improved sensing, adaptive control, and service-led business models suggests a transition from isolated pilots to commercially sustainable operations in contexts where labor economics, crop value, and logistical integration align. Stakeholders who proactively address supply chain risks, invest in human capital, and pursue modular architectures will be better positioned to capture the operational and quality benefits that these systems can deliver.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
189 Pages
- 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. Market Share Analysis, 2025
- 3.5. FPNV Positioning Matrix, 2025
- 3.6. New Revenue Opportunities
- 3.7. Next-Generation Business Models
- 3.8. 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. Porter’s Five Forces Analysis
- 4.4. PESTLE Analysis
- 4.5. Market Outlook
- 4.5.1. Near-Term Market Outlook (0–2 Years)
- 4.5.2. Medium-Term Market Outlook (3–5 Years)
- 4.5.3. Long-Term Market Outlook (5–10 Years)
- 4.6. 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 United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Fruit Picking Harvesting Robots Market, by Fruit Type
- 8.1. Apple
- 8.2. Berry
- 8.3. Citrus
- 8.4. Grape
- 8.5. Stone Fruit
- 9. Fruit Picking Harvesting Robots Market, by Deployment
- 9.1. Mobile
- 9.2. Stationary
- 10. Fruit Picking Harvesting Robots Market, by Technology
- 10.1. Ai Machine Learning
- 10.2. Force Control
- 10.3. Sensor Integration
- 10.4. Vision Guidance
- 10.4.1. 2d Vision
- 10.4.2. 3d Vision
- 11. Fruit Picking Harvesting Robots Market, by Application
- 11.1. Harvesting
- 11.2. Pruning
- 11.3. Sorting
- 11.4. Thinning
- 12. Fruit Picking Harvesting Robots Market, by End User
- 12.1. Farms
- 12.2. Orchards
- 12.3. Vineyards
- 13. Fruit Picking Harvesting Robots Market, by Autonomy
- 13.1. Autonomous
- 13.2. Semi Autonomous
- 14. Fruit Picking Harvesting Robots Market, by Offering
- 14.1. Hardware
- 14.2. Services
- 14.2.1. Consulting
- 14.2.2. Maintenance
- 14.2.3. Training
- 14.3. Software
- 15. Fruit Picking Harvesting Robots Market, by Region
- 15.1. Americas
- 15.1.1. North America
- 15.1.2. Latin America
- 15.2. Europe, Middle East & Africa
- 15.2.1. Europe
- 15.2.2. Middle East
- 15.2.3. Africa
- 15.3. Asia-Pacific
- 16. Fruit Picking Harvesting Robots Market, by Group
- 16.1. ASEAN
- 16.2. GCC
- 16.3. European Union
- 16.4. BRICS
- 16.5. G7
- 16.6. NATO
- 17. Fruit Picking Harvesting Robots Market, by Country
- 17.1. United States
- 17.2. Canada
- 17.3. Mexico
- 17.4. Brazil
- 17.5. United Kingdom
- 17.6. Germany
- 17.7. France
- 17.8. Russia
- 17.9. Italy
- 17.10. Spain
- 17.11. China
- 17.12. India
- 17.13. Japan
- 17.14. Australia
- 17.15. South Korea
- 18. United States Fruit Picking Harvesting Robots Market
- 19. China Fruit Picking Harvesting Robots Market
- 20. Competitive Landscape
- 20.1. Market Concentration Analysis, 2025
- 20.1.1. Concentration Ratio (CR)
- 20.1.2. Herfindahl Hirschman Index (HHI)
- 20.2. Recent Developments & Impact Analysis, 2025
- 20.3. Product Portfolio Analysis, 2025
- 20.4. Benchmarking Analysis, 2025
- 20.5. Abundant Robotics, Inc.
- 20.6. Advanced Farm Technologies, Inc.
- 20.7. Agrobot, S.L.
- 20.8. Antobot, Inc.
- 20.9. Augean Robotics, Inc.
- 20.10. Dogtooth Technologies, Inc.
- 20.11. Ecorobotix SA
- 20.12. FFRobotics, Inc.
- 20.13. Fieldwork Robotics Ltd.
- 20.14. Harvest Automation, Inc.
- 20.15. Harvest CROO, LLC
- 20.16. Iron Ox, Inc.
- 20.17. MetoMotion, Inc.
- 20.18. Muddy Machines, Inc.
- 20.19. Naïo Technologies SAS
- 20.20. Octinion, NV
- 20.21. Organifarms, Inc.
- 20.22. Priva Kompano B.V.
- 20.23. Ripe Robotics, Inc.
- 20.24. Robotics Plus Ltd
- 20.25. RoboVeg Ltd.
- 20.26. Tevel Aerobotics Technologies Ltd.
- 20.27. Tortuga Agricultural Technologies, Inc.
- 20.28. Xihelm, Inc.
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