커넥티드 농업 시장 : 컴포넌트별, 모듈별, 농장 유형별, 도입 형태별, 용도별, 최종 사용자별 시장 예측(2026-2032년)

The Connected Agriculture Market is projected to grow by USD 18.76 billion at a CAGR of 16.79% by 2032.

Connected Agriculture Executive Summary

Connected agriculture is the convergence of farm operations, Internet of Things sensors, satellite positioning, remote sensing, farm management software, cloud platforms, edge computing, automation, and data analytics. It is gaining strategic importance as producers work to raise productivity while managing climate volatility, water scarcity, labor constraints, input cost pressure, and tighter food traceability requirements.

Verified global indicators reinforce the urgency: the United Nations projects the world population will approach 9.7 billion by 2050, the FAO estimates food production must increase significantly to meet long-term demand, and the World Bank identifies agriculture as the largest user of global freshwater withdrawals. These pressures are accelerating adoption of precision agriculture, smart irrigation, livestock monitoring, connected machinery, digital agronomy, remote sensing, and AI-enabled decision support across crop, livestock, and aquaculture systems.

Transformative Shifts in the Connected Agriculture Landscape

The connected agriculture landscape is shifting from standalone precision tools to integrated digital farm ecosystems. Growers increasingly want platforms that combine machine data, soil and weather intelligence, satellite imagery, input records, field scouting, irrigation systems, and enterprise resource planning into one operational view. This shift is moving the industry from reactive farm management to predictive, evidence-based decision-making.

At the same time, interoperability, rural broadband, data ownership, and cybersecurity have become strategic priorities. Open APIs, low-power wide-area networks, 5G, satellite connectivity, and edge devices are reshaping how farms collect and use data, while sustainability reporting, climate-smart agriculture, and supply chain transparency are pushing digital adoption beyond the farm gate.

Cumulative Impact of Artificial Intelligence on Connected Agriculture

Artificial intelligence is compounding the value of connected agriculture by turning high-volume field, weather, equipment, soil, livestock, and market data into actionable recommendations. AI supports crop scouting through computer vision, irrigation scheduling through predictive models, yield estimation, pest and disease detection, autonomous machinery, variable-rate applications, and livestock health monitoring.

The cumulative impact is strongest when AI is paired with verified agronomic data, local calibration, reliable connectivity, and human oversight. Industry leaders are prioritizing explainable models, secure data pipelines, bias reduction, and farmer-friendly interfaces because adoption depends on trust, affordability, and measurable improvements in input efficiency, resilience, compliance, and profitability.

Key Regional Insights for Connected Agriculture

Asia-Pacific is a high-priority arena for connected agriculture due to large farming populations, food security priorities, and government-backed digital agriculture programs in China, India, Japan, South Korea, and Australia. Adoption ranges from smartphone-based advisory services for smallholders to robotics, protected cultivation, agricultural drones, and satellite-driven precision farming in advanced markets, with water management and labor efficiency remaining central drivers.

North America remains one of the most mature regions, supported by large-scale mechanized farms, strong rural equipment ecosystems, GPS-guided operations, crop insurance integration, and rising use of AI-enabled analytics in the United States and Canada. Latin America is gaining momentum as Brazil, Mexico, and other producers digitize large row-crop, livestock, horticulture, and export-oriented operations to improve yield visibility, traceability, logistics, and input optimization.

Europe is shaped by sustainability regulation, the Common Agricultural Policy, environmental compliance, and strong interest in traceability, soil health, biodiversity protection, and lower chemical inputs. The Middle East is prioritizing connected irrigation, greenhouse automation, desalination-linked water management, and controlled-environment agriculture to address water scarcity and food import dependence, while Africa is advancing through mobile advisory services, digital finance, weather intelligence, geospatial monitoring, and remote sensing that support smallholder resilience and climate adaptation.

Key Group Insights Across ASEAN, GCC, EU, BRICS, G7, and NATO

ASEAN markets are advancing connected agriculture through mobile-first farm advisory, digital marketplaces, smart aquaculture, e-extension services, and climate information platforms, particularly where smallholder productivity, rice resilience, aquaculture output, and food security are policy priorities. The GCC is focused on water-efficient agriculture, desalination-linked irrigation management, greenhouse automation, vertical farming, and controlled-environment food production, reflecting structural aridity, limited arable land, and high import dependence.

The European Union is a major driver of digital sustainability through precision input use, farm data spaces, traceability, climate-aligned agricultural policy, and support for soil and biodiversity monitoring. BRICS countries combine large food production bases with national priorities around mechanization, domestic technology capacity, digital public infrastructure, and resilient supply chains, creating demand for scalable, cost-efficient connected agriculture tools that can serve both large farms and smallholders.

G7 economies lead in advanced machinery, data platforms, AI governance, agri-food innovation funding, and sustainability-linked digital reporting. NATO members increasingly view food systems, cyber resilience, logistics, satellite infrastructure, and critical infrastructure protection as part of broader security planning, which reinforces demand for secure connected agriculture networks, protected data flows, and resilient agri-food supply chains.

Key Country Insights for Connected Agriculture

The United States leads in precision agriculture, connected machinery, farm software, autonomous equipment trials, and data-driven row-crop management, while Canada applies connected tools to broadacre crops, livestock, grain logistics, and climate-resilient production. Mexico is expanding digital agriculture across horticulture, protected cultivation, irrigation management, and export supply chains where traceability and quality assurance are critical.

Brazil is a major adoption center for connected agriculture in soybeans, corn, sugarcane, coffee, cotton, and cattle, supported by large-scale operations, tropical agronomy needs, and strong agri-tech entrepreneurship. The United Kingdom, Germany, France, Italy, and Spain are emphasizing sustainable intensification, traceability, robotics, smart irrigation, precision viticulture, and compliance with evolving environmental standards. Russia's large grain and oilseed sector creates demand for fleet management, satellite monitoring, storage visibility, and logistics optimization across vast production areas.

China is scaling smart farming, agricultural drones, BeiDou-enabled positioning, digital rural infrastructure, and data-supported food security initiatives. India is advancing mobile advisory, digital public infrastructure, remote sensing, weather services, and smallholder-focused platforms that support crop planning and financial inclusion. Japan and South Korea are leaders in robotics, smart greenhouses, labor-saving automation, and high-tech protected cultivation, while Australia applies connected agriculture to broadacre farming, livestock monitoring, water management, rangeland analytics, and drought resilience.

Actionable Recommendations for Industry Leaders

Industry leaders should prioritize interoperable platforms that integrate equipment, sensors, satellite imagery, agronomic models, irrigation systems, livestock devices, and enterprise systems. Farmers need solutions that reduce operational complexity, not another disconnected dashboard. Vendors should build open integrations, flexible pricing, multilingual support, offline-capable workflows, and tools designed for low-connectivity rural environments.

Organizations should quantify return on investment through input savings, yield stability, water productivity, labor efficiency, compliance value, traceability gains, and risk reduction. Partnerships with cooperatives, agronomists, telecom providers, insurers, equipment manufacturers, financial institutions, and public agencies can accelerate adoption. Cybersecurity, data governance, model transparency, farmer consent, and clear data ownership terms must be embedded from the beginning to strengthen trust and long-term use.

Research Methodology

This executive summary is based on a structured research approach that combines secondary research, primary validation, and analytical triangulation. Inputs include public datasets and publications from organizations such as FAO, World Bank, OECD, ITU, GSMA, national agriculture departments, standards bodies, extension agencies, and peer-reviewed agricultural technology literature.

The methodology evaluates demand drivers, technology adoption patterns, regional policy frameworks, connectivity readiness, sustainability requirements, competitive activity, and end-user needs across crop, livestock, aquaculture, and controlled-environment agriculture. Findings are cross-checked against multiple credible sources to maintain data integrity, reduce bias, and support practical interpretation without relying on market sizing or forecasting.

Conclusion

Connected agriculture is becoming a core enabler of resilient, productive, resource-efficient, and transparent food systems. As climate stress, input volatility, labor shortages, water scarcity, and sustainability requirements intensify, digital tools are moving from optional enhancements to operational necessities across the agri-food value chain.

The next phase of adoption will favor solutions that are interoperable, secure, locally relevant, AI-enabled, and economically accessible. Organizations that combine agronomic credibility with scalable digital infrastructure, trusted data governance, and measurable farm-level outcomes will be best positioned to create long-term value in connected agriculture.

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. 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. 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. Connected Agriculture Market, by Component

• 7.1. Services
  • 7.1.1. Consulting Services
  • 7.1.2. Managed Services
  • 7.1.3. Professional Services
  • 7.1.4. Support & Maintenance
  • 7.1.5. Training & Education
• 7.2. Solutions
  • 7.2.1. Crop Monitoring
  • 7.2.2. Farming Machinery Management
  • 7.2.3. Greenhouse Automation
  • 7.2.4. Irrigation Management
  • 7.2.5. Livestock Monitoring
  • 7.2.6. Weather & Soil Monitoring

8. Connected Agriculture Market, by Module

• 8.1. Application Enablement
• 8.2. Connectivity Management
• 8.3. Device Management

9. Connected Agriculture Market, by Farm Type

• 9.1. Crop Farms
• 9.2. Livestock Farms
• 9.3. Aquaculture Farms

10. Connected Agriculture Market, by Deployment

• 10.1. Cloud
• 10.2. On-Premises

11. Connected Agriculture Market, by Application

• 11.1. In-Production Planning & Management
• 11.2. Post-Production Planning & Management
• 11.3. Pre-Production Planning & Management

12. Connected Agriculture Market, by End User

• 12.1. Farmers
• 12.2. Government Agencies
• 12.3. Research Institutions

13. Connected Agriculture Market, by Region

• 13.1. Asia-Pacific
• 13.2. North America
• 13.3. Latin America
• 13.4. Europe
• 13.5. Middle East
• 13.6. Africa

14. Connected Agriculture Market, by Group

• 14.1. ASEAN
• 14.2. GCC
• 14.3. European Union
• 14.4. BRICS
• 14.5. G7
• 14.6. NATO

15. Connected Agriculture Market, by Country

• 15.1. United States
• 15.2. Canada
• 15.3. Mexico
• 15.4. Brazil
• 15.5. United Kingdom
• 15.6. Germany
• 15.7. France
• 15.8. Russia
• 15.9. Italy
• 15.10. Spain
• 15.11. China
• 15.12. India
• 15.13. Japan
• 15.14. Australia
• 15.15. South Korea

16. Competitive Landscape

• 16.1. Market Concentration Analysis, 2025
  • 16.1.1. Concentration Ratio (CR)
  • 16.1.2. Herfindahl Hirschman Index (HHI)
• 16.2. Recent Developments & Impact Analysis, 2025
• 16.3. Product Portfolio Analysis, 2025
• 16.4. Benchmarking Analysis, 2025

17. Company Profiles

• 17.1. Accenture PLC
• 17.2. Ag Leader Technology
• 17.3. AGCO Corporation
• 17.4. AGRIVI Ltd.
• 17.5. BASF SE
• 17.6. Bayer AG
• 17.7. Cisco Systems, Inc.
• 17.8. CNH Industrial N.V.
• 17.9. Conservis By Traction Ag Inc.
• 17.10. Corteva, Inc.
• 17.11. Cropin Technology Solutions Private Limited
• 17.12. Croptracker, Inc.
• 17.13. Deere & Company
• 17.14. Farmers Edge Inc.
• 17.15. Gamaya
• 17.16. Granular Inc.
• 17.17. Hexagon AB
• 17.18. International Business Machines Corporation
• 17.19. Kubota Corporation
• 17.20. Lindsay Corporation
• 17.21. Microsoft Corporation
• 17.22. Oracle Corporation
• 17.23. Robert Bosch GmbH
• 17.24. SAP SE
• 17.25. Shivrai Technologies Pvt. Ltd.
• 17.26. SMAG by InVivo
• 17.27. Syngenta AG
• 17.28. Taranis
• 17.29. TELUS Communications Inc.
• 17.30. Tend.com
• 17.31. Topcon Corporation
• 17.32. Yara International ASA