Agriculture Technology-as-a-Service Market by Service Type (Data Analytics Services, Farm Management Software Services, IoT Device Monitoring Services), Business Model (Outcome Based, Pay Per Use, Subscription), Crop Type, Farm Size, Application, End User
Description
The Agriculture Technology-as-a-Service Market was valued at USD 419.87 million in 2024 and is projected to grow to USD 447.77 million in 2025, with a CAGR of 6.75%, reaching USD 708.49 million by 2032.
An incisive orientation to the evolving Agriculture Technology-as-a-Service paradigm that contextualizes operational drivers, stakeholder incentives, and adoption constraints
The agriculture technology-as-a-service landscape is undergoing a rapid evolution driven by converging digital, environmental, and economic pressures. Farmers and agribusinesses increasingly prioritize solutions that convert raw data into operational decisions, enabling more precise input management, enhanced traceability, and resilient supply chains. As capital shifts from standalone hardware purchases toward integrated service models, decision-makers are evaluating the trade-offs between short-term operating costs and long-term productivity and sustainability outcomes.
This introduction frames the report's focus on service delivery models that integrate sensors, analytics, and workflows to deliver measurable outcomes. It also outlines the methodological lens used throughout the analysis: an emphasis on end-user value creation, interoperability, and the alignment of incentives across supply chains. By clarifying the strategic imperatives and the practical constraints that operators face in adoption, this section primes stakeholders to consider both technological readiness and organizational change management as they evaluate potential investments.
How connectivity, modular software architectures, and outcome-oriented commercial models are jointly reshaping service delivery and adoption across agricultural value chains
Innovations in connectivity, edge compute, and artificial intelligence are creating transformative shifts in how agricultural services are delivered and consumed. The proliferation of low-cost sensors and the maturation of remote sensing techniques are reducing the latency between data acquisition and decision-making, enabling adaptive strategies for irrigation, pest control, and nutrient application. At the same time, software platforms are moving toward modular, API-first designs that prioritize data portability and third-party integrations, which in turn accelerates ecosystem development and reduces vendor lock-in.
Concurrently, commercial models are shifting from product-centric to outcome-driven arrangements that align payments with agronomic performance or resource savings. This shift encourages providers to optimize not only for feature sets but for measurable yields and cost reductions. Regulatory and sustainability pressures are reinforcing these transitions by emphasizing traceability and emissions accounting, thereby elevating technologies that support transparent record-keeping and verifiable outcomes. As a result, service providers are reconfiguring their offerings to combine sensing, analytics, and advisory services into bundled solutions that deliver clear operational value.
Assessing how tariff-driven procurement pressures and regionalization efforts in 2025 are reshaping supply chains, pricing structures, and risk management for agricultural technology services
The imposition of tariffs and trade policy shifts in 2025 has introduced new considerations for supply chains that support agricultural technology services. Many key components, including advanced sensors, certain classes of semiconductor components, and specialized mechanical parts, are integrated into devices and platforms procured through international supply chains. As tariffs alter landed costs and supplier selection, providers are adjusting procurement strategies to manage margin pressure while preserving service reliability. This results in a dual response: diversification of component suppliers across geographies and increased investment in design for supply chain resilience.
Moreover, downstream service models adapt to mitigate cost volatility for end users. Providers that operate on subscription or outcome-based contracts may absorb short-term tariff-driven cost increases to maintain customer retention, while others restructure pricing to include explicit hardware surcharges or multi-year contracts with built-in cost pass-through clauses. In parallel, there is an acceleration in regional manufacturing and assembly initiatives that aim to localize critical production steps and reduce exposure to trade policy shifts. These responses are accompanied by enhanced contractual risk management practices, including hedging strategies and supplier performance guarantees, which together aim to stabilize service continuity and protect adoption momentum.
A detailed segmentation perspective that aligns service capabilities, application demands, business model incentives, end-user procurement complexity, crop specificity, and farm-size economics
A granular segmentation framework reveals how value propositions must be tailored to distinct service, application, business model, end-user, crop type, and farm-size profiles. Based on service type, offerings vary from Data Analytics Services that deliver descriptive, predictive, and prescriptive analytics to Farm Management Software Services that encompass compliance, financial, inventory, and workforce management, as well as IoT Device Monitoring Services, Precision Farming Services, and Remote Sensing Services; providers must therefore calibrate data latency, model explainability, and integration depth to each subsegment. In practice, descriptive analytics serve diagnostic and benchmarking needs, predictive analytics inform risk mitigation and scheduling, while prescriptive analytics drive automated recommendation engines that tie directly to input application and yield optimization.
When examined by application, deployments range from Crop Monitoring that leverages drone imaging, ground sensors, and satellite imaging to irrigation, livestock, pest, and supply chain management, and each application imposes different requirements for data fidelity, spatial resolution, and real-time responsiveness. Business model segmentation-Outcome Based, Pay Per Use, and Subscription-affects cash flow dynamics and provider incentives, necessitating different SLAs, data governance constructs, and performance measurement frameworks. End-user distinctions among agribusinesses, cooperatives, government agencies, and growers determine procurement complexity and customization demands, while crop type segmentation into horticulture, orchards, row crops, and specialty crops influences sensor density, phenology models, and advisory cadence. Finally, farm size categories from large to micro and small farms drive unit economics and feature prioritization, with smaller operators favoring simplified interfaces and turnkey installation while larger farms demand integration at scale and advanced analytics pipelines.
Regional strategic imperatives that influence technology adoption, regulatory compliance, and deployment architectures across the Americas, Europe Middle East & Africa, and Asia-Pacific
Regional dynamics shape priorities for technology adoption, regulatory compliance, and partnership strategies across the Americas, Europe, Middle East & Africa, and Asia-Pacific. In the Americas, emphasis centers on large-scale mechanized operations and vertically integrated agribusinesses that require scalable platforms, sophisticated yield models, and deep integration with commodity trading and logistics systems, which encourages robust API connectivity and enterprise-grade data security. Conversely, Europe, Middle East & Africa presents a mosaic of regulatory regimes and sustainability mandates that prioritize traceability, emissions accounting, and resource efficiency, fostering demand for solutions that support certification, auditability, and cross-border data interoperability.
The Asia-Pacific region combines high-growth agricultural sectors with a broad spectrum of farm sizes, creating a need for both highly scalable cloud services and lightweight offline-capable solutions. Regional supply chain configurations also influence localization strategies; for example, sensor and device availability, local manufacturing capabilities, and regional cloud infrastructure choices affect time-to-deploy and total cost of ownership considerations. Collectively, these regional distinctions require providers to adopt flexible deployment architectures, modular pricing, and a network of local partnerships to effectively serve heterogeneous customer bases while complying with varying regulatory expectations.
Strategic corporate moves toward platform extensibility, channel alliances, and outcome-aligned partnerships that drive differentiation and commercial resilience
Corporate strategies in the space emphasize platform extensibility, channel partnerships, and the alignment of incentives with agricultural outcomes. Leading firms concentrate on building interoperable platforms that support third-party integrations and developer ecosystems, thereby expanding use cases and accelerating innovation through partner-delivered modules. At the same time, firms that focus on hardware differentiate through total cost of ownership optimizations, long-term maintenance agreements, and bundled analytics that translate sensor signals into actionable recommendations. Strategic alliances between technology providers, input suppliers, and financial institutions are becoming increasingly important, as they enable innovative bundled offers that combine finance, inputs, and advisory services tied to performance metrics.
Consolidation activity manifests in targeted acquisitions that add complementary analytics capabilities, domain expertise in specific crop types, or regional go-to-market channels. Companies adopting outcome-based models are investing in robust measurement frameworks and independent verification to support transparent payout structures. In parallel, those operating subscription or pay-per-use models invest in customer success operations and field support to reduce churn and improve lifetime value, recognizing that service quality and responsiveness are primary determinants of long-term commercial viability in agricultural contexts.
Actionable strategic priorities for providers to accelerate adoption including interoperability investments, outcome-aligned commercial models, and scaled local partnership networks
Industry leaders should prioritize three interlocking strategic moves to capture value and accelerate adoption: invest in data interoperability and open APIs to reduce friction for enterprise integration, design commercial terms that align incentives with agronomic outcomes, and scale local partnership networks to manage deployment complexity. By investing in interoperable architectures, providers reduce customer switching costs and foster ecosystems of complementary services, enabling faster innovation and improved retention. Simultaneously, aligning commercial terms with observable outcomes incentivizes continuous performance improvements and strengthens client trust, especially when contracts transparently allocate performance risk and measurement responsibilities.
Scaling local partnerships-ranging from input distributors to extension services and regional integrators-allows providers to adapt offerings to local agronomic conditions and regulatory regimes while maintaining consistent global standards. Leaders should also develop clear playbooks for migrating pilots to scaled deployments, including technical onboarding templates, ROI mapping tools, and localized training curricula. Finally, operational excellence in field support, data governance, and customer success will differentiate providers as the market moves from experimentation to mainstream adoption.
A rigorous mixed-methods research methodology combining primary interviews, secondary validation, triangulation, value-chain mapping, and scenario analysis to produce actionable insights
The research approach integrates qualitative and quantitative methods to ensure robust, defensible insights that are actionable for decision-makers. Primary research included structured interviews with executives across agricultural service providers, agribusiness procurement leads, and end users to ground findings in practitioner experience. Secondary sources encompassed peer-reviewed literature, governmental policy releases, and industry white papers to validate technological trajectories and regulatory influences. Data triangulation was performed across multiple inputs to reconcile divergent perspectives and isolate observable trends that are replicable across regions and use cases.
Analytical techniques included value chain mapping to identify bottlenecks and opportunity nodes, scenario analysis to stress-test commercial models under different policy and cost assumptions, and use-case benchmarking to assess solution readiness for specific applications such as irrigation management and crop monitoring. Quality assurance processes included cross-validation of vendor claims, verification of technical specifications against independent test reports, and iterative review cycles with subject matter experts to refine assumptions and interpretations. Limitations and uncertainty bounds are documented to guide appropriate application of the findings in strategic planning and procurement decisions.
A concise summation of how integrated solutions, outcome-driven contracts, and supply chain resilience will determine competitive advantage and adoption trajectories in the sector
In conclusion, the Agriculture Technology-as-a-Service sector stands at an inflection point where technological maturity, commercial innovation, and regulatory pressure are converging to transform how agricultural outcomes are achieved and measured. The path forward favors providers that can demonstrate measurable value through integrated solutions, align commercial incentives with client performance, and manage supply chain risks through diversification and regionalization. End users are increasingly seeking partners that offer not only technology but also pragmatic deployment and support frameworks that reduce operational complexity and deliver verifiable outcomes.
As stakeholders navigate this evolving landscape, the emphasis should be on pragmatic experimentation, rigorous measurement, and scalable partnerships. Providers that combine interoperable platforms with outcome-driven contracts and strong local execution capabilities will have the strongest prospects for durable competitive advantage. Policymakers and industry stakeholders should likewise emphasize standards for data portability and verification to support transparent, scalable adoption that benefits producers and supply chain participants alike.
Note: PDF & Excel + Online Access - 1 Year
An incisive orientation to the evolving Agriculture Technology-as-a-Service paradigm that contextualizes operational drivers, stakeholder incentives, and adoption constraints
The agriculture technology-as-a-service landscape is undergoing a rapid evolution driven by converging digital, environmental, and economic pressures. Farmers and agribusinesses increasingly prioritize solutions that convert raw data into operational decisions, enabling more precise input management, enhanced traceability, and resilient supply chains. As capital shifts from standalone hardware purchases toward integrated service models, decision-makers are evaluating the trade-offs between short-term operating costs and long-term productivity and sustainability outcomes.
This introduction frames the report's focus on service delivery models that integrate sensors, analytics, and workflows to deliver measurable outcomes. It also outlines the methodological lens used throughout the analysis: an emphasis on end-user value creation, interoperability, and the alignment of incentives across supply chains. By clarifying the strategic imperatives and the practical constraints that operators face in adoption, this section primes stakeholders to consider both technological readiness and organizational change management as they evaluate potential investments.
How connectivity, modular software architectures, and outcome-oriented commercial models are jointly reshaping service delivery and adoption across agricultural value chains
Innovations in connectivity, edge compute, and artificial intelligence are creating transformative shifts in how agricultural services are delivered and consumed. The proliferation of low-cost sensors and the maturation of remote sensing techniques are reducing the latency between data acquisition and decision-making, enabling adaptive strategies for irrigation, pest control, and nutrient application. At the same time, software platforms are moving toward modular, API-first designs that prioritize data portability and third-party integrations, which in turn accelerates ecosystem development and reduces vendor lock-in.
Concurrently, commercial models are shifting from product-centric to outcome-driven arrangements that align payments with agronomic performance or resource savings. This shift encourages providers to optimize not only for feature sets but for measurable yields and cost reductions. Regulatory and sustainability pressures are reinforcing these transitions by emphasizing traceability and emissions accounting, thereby elevating technologies that support transparent record-keeping and verifiable outcomes. As a result, service providers are reconfiguring their offerings to combine sensing, analytics, and advisory services into bundled solutions that deliver clear operational value.
Assessing how tariff-driven procurement pressures and regionalization efforts in 2025 are reshaping supply chains, pricing structures, and risk management for agricultural technology services
The imposition of tariffs and trade policy shifts in 2025 has introduced new considerations for supply chains that support agricultural technology services. Many key components, including advanced sensors, certain classes of semiconductor components, and specialized mechanical parts, are integrated into devices and platforms procured through international supply chains. As tariffs alter landed costs and supplier selection, providers are adjusting procurement strategies to manage margin pressure while preserving service reliability. This results in a dual response: diversification of component suppliers across geographies and increased investment in design for supply chain resilience.
Moreover, downstream service models adapt to mitigate cost volatility for end users. Providers that operate on subscription or outcome-based contracts may absorb short-term tariff-driven cost increases to maintain customer retention, while others restructure pricing to include explicit hardware surcharges or multi-year contracts with built-in cost pass-through clauses. In parallel, there is an acceleration in regional manufacturing and assembly initiatives that aim to localize critical production steps and reduce exposure to trade policy shifts. These responses are accompanied by enhanced contractual risk management practices, including hedging strategies and supplier performance guarantees, which together aim to stabilize service continuity and protect adoption momentum.
A detailed segmentation perspective that aligns service capabilities, application demands, business model incentives, end-user procurement complexity, crop specificity, and farm-size economics
A granular segmentation framework reveals how value propositions must be tailored to distinct service, application, business model, end-user, crop type, and farm-size profiles. Based on service type, offerings vary from Data Analytics Services that deliver descriptive, predictive, and prescriptive analytics to Farm Management Software Services that encompass compliance, financial, inventory, and workforce management, as well as IoT Device Monitoring Services, Precision Farming Services, and Remote Sensing Services; providers must therefore calibrate data latency, model explainability, and integration depth to each subsegment. In practice, descriptive analytics serve diagnostic and benchmarking needs, predictive analytics inform risk mitigation and scheduling, while prescriptive analytics drive automated recommendation engines that tie directly to input application and yield optimization.
When examined by application, deployments range from Crop Monitoring that leverages drone imaging, ground sensors, and satellite imaging to irrigation, livestock, pest, and supply chain management, and each application imposes different requirements for data fidelity, spatial resolution, and real-time responsiveness. Business model segmentation-Outcome Based, Pay Per Use, and Subscription-affects cash flow dynamics and provider incentives, necessitating different SLAs, data governance constructs, and performance measurement frameworks. End-user distinctions among agribusinesses, cooperatives, government agencies, and growers determine procurement complexity and customization demands, while crop type segmentation into horticulture, orchards, row crops, and specialty crops influences sensor density, phenology models, and advisory cadence. Finally, farm size categories from large to micro and small farms drive unit economics and feature prioritization, with smaller operators favoring simplified interfaces and turnkey installation while larger farms demand integration at scale and advanced analytics pipelines.
Regional strategic imperatives that influence technology adoption, regulatory compliance, and deployment architectures across the Americas, Europe Middle East & Africa, and Asia-Pacific
Regional dynamics shape priorities for technology adoption, regulatory compliance, and partnership strategies across the Americas, Europe, Middle East & Africa, and Asia-Pacific. In the Americas, emphasis centers on large-scale mechanized operations and vertically integrated agribusinesses that require scalable platforms, sophisticated yield models, and deep integration with commodity trading and logistics systems, which encourages robust API connectivity and enterprise-grade data security. Conversely, Europe, Middle East & Africa presents a mosaic of regulatory regimes and sustainability mandates that prioritize traceability, emissions accounting, and resource efficiency, fostering demand for solutions that support certification, auditability, and cross-border data interoperability.
The Asia-Pacific region combines high-growth agricultural sectors with a broad spectrum of farm sizes, creating a need for both highly scalable cloud services and lightweight offline-capable solutions. Regional supply chain configurations also influence localization strategies; for example, sensor and device availability, local manufacturing capabilities, and regional cloud infrastructure choices affect time-to-deploy and total cost of ownership considerations. Collectively, these regional distinctions require providers to adopt flexible deployment architectures, modular pricing, and a network of local partnerships to effectively serve heterogeneous customer bases while complying with varying regulatory expectations.
Strategic corporate moves toward platform extensibility, channel alliances, and outcome-aligned partnerships that drive differentiation and commercial resilience
Corporate strategies in the space emphasize platform extensibility, channel partnerships, and the alignment of incentives with agricultural outcomes. Leading firms concentrate on building interoperable platforms that support third-party integrations and developer ecosystems, thereby expanding use cases and accelerating innovation through partner-delivered modules. At the same time, firms that focus on hardware differentiate through total cost of ownership optimizations, long-term maintenance agreements, and bundled analytics that translate sensor signals into actionable recommendations. Strategic alliances between technology providers, input suppliers, and financial institutions are becoming increasingly important, as they enable innovative bundled offers that combine finance, inputs, and advisory services tied to performance metrics.
Consolidation activity manifests in targeted acquisitions that add complementary analytics capabilities, domain expertise in specific crop types, or regional go-to-market channels. Companies adopting outcome-based models are investing in robust measurement frameworks and independent verification to support transparent payout structures. In parallel, those operating subscription or pay-per-use models invest in customer success operations and field support to reduce churn and improve lifetime value, recognizing that service quality and responsiveness are primary determinants of long-term commercial viability in agricultural contexts.
Actionable strategic priorities for providers to accelerate adoption including interoperability investments, outcome-aligned commercial models, and scaled local partnership networks
Industry leaders should prioritize three interlocking strategic moves to capture value and accelerate adoption: invest in data interoperability and open APIs to reduce friction for enterprise integration, design commercial terms that align incentives with agronomic outcomes, and scale local partnership networks to manage deployment complexity. By investing in interoperable architectures, providers reduce customer switching costs and foster ecosystems of complementary services, enabling faster innovation and improved retention. Simultaneously, aligning commercial terms with observable outcomes incentivizes continuous performance improvements and strengthens client trust, especially when contracts transparently allocate performance risk and measurement responsibilities.
Scaling local partnerships-ranging from input distributors to extension services and regional integrators-allows providers to adapt offerings to local agronomic conditions and regulatory regimes while maintaining consistent global standards. Leaders should also develop clear playbooks for migrating pilots to scaled deployments, including technical onboarding templates, ROI mapping tools, and localized training curricula. Finally, operational excellence in field support, data governance, and customer success will differentiate providers as the market moves from experimentation to mainstream adoption.
A rigorous mixed-methods research methodology combining primary interviews, secondary validation, triangulation, value-chain mapping, and scenario analysis to produce actionable insights
The research approach integrates qualitative and quantitative methods to ensure robust, defensible insights that are actionable for decision-makers. Primary research included structured interviews with executives across agricultural service providers, agribusiness procurement leads, and end users to ground findings in practitioner experience. Secondary sources encompassed peer-reviewed literature, governmental policy releases, and industry white papers to validate technological trajectories and regulatory influences. Data triangulation was performed across multiple inputs to reconcile divergent perspectives and isolate observable trends that are replicable across regions and use cases.
Analytical techniques included value chain mapping to identify bottlenecks and opportunity nodes, scenario analysis to stress-test commercial models under different policy and cost assumptions, and use-case benchmarking to assess solution readiness for specific applications such as irrigation management and crop monitoring. Quality assurance processes included cross-validation of vendor claims, verification of technical specifications against independent test reports, and iterative review cycles with subject matter experts to refine assumptions and interpretations. Limitations and uncertainty bounds are documented to guide appropriate application of the findings in strategic planning and procurement decisions.
A concise summation of how integrated solutions, outcome-driven contracts, and supply chain resilience will determine competitive advantage and adoption trajectories in the sector
In conclusion, the Agriculture Technology-as-a-Service sector stands at an inflection point where technological maturity, commercial innovation, and regulatory pressure are converging to transform how agricultural outcomes are achieved and measured. The path forward favors providers that can demonstrate measurable value through integrated solutions, align commercial incentives with client performance, and manage supply chain risks through diversification and regionalization. End users are increasingly seeking partners that offer not only technology but also pragmatic deployment and support frameworks that reduce operational complexity and deliver verifiable outcomes.
As stakeholders navigate this evolving landscape, the emphasis should be on pragmatic experimentation, rigorous measurement, and scalable partnerships. Providers that combine interoperable platforms with outcome-driven contracts and strong local execution capabilities will have the strongest prospects for durable competitive advantage. Policymakers and industry stakeholders should likewise emphasize standards for data portability and verification to support transparent, scalable adoption that benefits producers and supply chain participants alike.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
181 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of AI-driven pest detection systems for proactive disease management in crops
- 5.2. Scaling modular vertical farming solutions with IoT-enabled climate control across urban centers
- 5.3. Deployment of subscription-based precision irrigation platforms optimizing water usage
- 5.4. Utilization of blockchain traceability networks connecting farm-to-consumer supply chains
- 5.5. Expansion of autonomous drone fleets providing multispectral imaging and spraying services
- 5.6. Implementation of soil health monitoring subscriptions using sensor networks and data analytics
- 5.7. Development of agronomic insights platforms leveraging machine learning for yield prediction
- 5.8. Partnerships between agritech SaaS providers and financial institutions for crop financing tools
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Agriculture Technology-as-a-Service Market, by Service Type
- 8.1. Data Analytics Services
- 8.1.1. Descriptive Analytics
- 8.1.2. Predictive Analytics
- 8.1.3. Prescriptive Analytics
- 8.2. Farm Management Software Services
- 8.2.1. Compliance Management
- 8.2.2. Financial Management
- 8.2.3. Inventory Management
- 8.2.4. Workforce Management
- 8.3. IoT Device Monitoring Services
- 8.4. Precision Farming Services
- 8.5. Remote Sensing Services
- 9. Agriculture Technology-as-a-Service Market, by Business Model
- 9.1. Outcome Based
- 9.2. Pay Per Use
- 9.3. Subscription
- 10. Agriculture Technology-as-a-Service Market, by Crop Type
- 10.1. Horticulture
- 10.2. Orchards
- 10.3. Row Crops
- 10.4. Specialty Crops
- 11. Agriculture Technology-as-a-Service Market, by Farm Size
- 11.1. Large Farms
- 11.2. Medium Farms
- 11.3. Micro Farms
- 11.4. Small Farms
- 12. Agriculture Technology-as-a-Service Market, by Application
- 12.1. Crop Monitoring
- 12.1.1. Drone Imaging
- 12.1.2. Ground Sensors
- 12.1.3. Satellite Imaging
- 12.2. Irrigation Management
- 12.3. Livestock Management
- 12.4. Pest Management
- 12.5. Supply Chain Management
- 13. Agriculture Technology-as-a-Service Market, by End User
- 13.1. Agribusinesses
- 13.2. Cooperatives
- 13.3. Government Agencies
- 13.4. Growers
- 14. Agriculture Technology-as-a-Service Market, by Region
- 14.1. Americas
- 14.1.1. North America
- 14.1.2. Latin America
- 14.2. Europe, Middle East & Africa
- 14.2.1. Europe
- 14.2.2. Middle East
- 14.2.3. Africa
- 14.3. Asia-Pacific
- 15. Agriculture Technology-as-a-Service Market, by Group
- 15.1. ASEAN
- 15.2. GCC
- 15.3. European Union
- 15.4. BRICS
- 15.5. G7
- 15.6. NATO
- 16. Agriculture Technology-as-a-Service Market, by Country
- 16.1. United States
- 16.2. Canada
- 16.3. Mexico
- 16.4. Brazil
- 16.5. United Kingdom
- 16.6. Germany
- 16.7. France
- 16.8. Russia
- 16.9. Italy
- 16.10. Spain
- 16.11. China
- 16.12. India
- 16.13. Japan
- 16.14. Australia
- 16.15. South Korea
- 17. Competitive Landscape
- 17.1. Market Share Analysis, 2024
- 17.2. FPNV Positioning Matrix, 2024
- 17.3. Competitive Analysis
- 17.3.1. 365FarmNet GmbH
- 17.3.2. Accenture PLC
- 17.3.3. AG Leader Technology
- 17.3.4. AGCO Corporation
- 17.3.5. AgEagle Aerial Systems Inc.
- 17.3.6. AgJunction, Inc. by Kubota Corp.
- 17.3.7. AgriData Incorporated
- 17.3.8. AGRIVI d.o.o.
- 17.3.9. Aker Technologies by IntelinAir, Inc.
- 17.3.10. ATMOS UAV
- 17.3.11. Corteva Agriscience
- 17.3.12. Cropin Technology Solutions Pvt Ltd
- 17.3.13. Deere & Company
- 17.3.14. Delair SAS
- 17.3.15. Dickey-John Corporation
- 17.3.16. DroneDeploy, Inc.
- 17.3.17. Farmers Edge Precision Consulting Inc.
- 17.3.18. Hexagon AB
- 17.3.19. Kubota Corporation
- 17.3.20. Lockheed Martin Corporation
- 17.3.21. Microdrones GmbH by mdGroup GmbH
- 17.3.22. Nileworks Inc.
- 17.3.23. Parrot Drone S.A.S
- 17.3.24. PrecisionHawk, Inc.
- 17.3.25. Prospera Technologies Ltd. by Valmont Industries, Inc.
- 17.3.26. Raven Industries, Inc. by CNH Industrial N.V.
- 17.3.27. SZ DJI Technology Co., Ltd.
- 17.3.28. Teejet Technologies
- 17.3.29. Topcon Positioning Systems
- 17.3.30. Yamaha Motor Co., Ltd.
Pricing
Currency Rates
Questions or Comments?
Our team has the ability to search within reports to verify it suits your needs. We can also help maximize your budget by finding sections of reports you can purchase.
