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시장보고서
상품코드
2082132
스마트 농업 시장 : 제공 내용별, 접속성별, 기술별, 도입 형태별, 농장 규모별, 사용자 유형별, 용도별 시장 예측(2026-2032년)Smart Agriculture Market by Offering, Connectivity, Technology, Deployment Mode, Farm Size, User Type, Application - Global Forecast 2026-2032 |
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360iResearch
스마트 농업 시장은 2032년까지 연평균 복합 성장률(CAGR) 13.29%로 성장이 전망되며, 559억 1,000만 달러 규모로 확대될 것으로 예측됩니다.
| 주요 시장 통계 | |
|---|---|
| 기준 연도 : 2025년 | 233억 3,000만 달러 |
| 추정 연도 : 2026년 | 262억 9,000만 달러 |
| 예측 연도 : 2032년 | 559억 1,000만 달러 |
| CAGR(%) | 13.29% |
스마트 농업은 단순한 기술 분야에서 벗어나, 현대 식량 시스템의 핵심 운영 모델로 전환되고 있습니다. 이는 정밀 농업, 농장 관리 소프트웨어, IoT 센서, 위성 이미지, 가변 시비 장비, 자율형 농업 기계, 로봇 공학, 드론, 네트워크 기반 관개 시스템 및 고급 분석 기술을 결합하여 생산성, 자원 효율성, 추적성 및 기후 변화에 대한 회복력을 향상시키는 것입니다.
이 분야의 시급성은 측정 가능한 세계적 과제에 근거하고 있습니다. 유엔은 2050년까지 세계 인구가 약 97억 명에 달할 것으로 예측했습니다. 한편, 널리 인용되고 있는 FAO(유엔 식량농업기구) 및 세계은행의 자료에 따르면, 농업은 전 세계 담수 취수량의 약 70%를 차지하고 있습니다. 이러한 제약으로 인해 생산자, 농업 관련 기업, 정부, 기술 제공업체는 물, 비료, 에너지 및 노동 집약도를 줄이면서 수확량을 높일 수 있는 디지털 농업 솔루션의 도입을 서둘러야 하는 상황에 놓여 있습니다.
스마트 농업 분야의 혁신적인 변화
스마트 농업 분야는 연결된 기기, 클라우드 플랫폼, 위성 모니터링, 그리고 디지털 농업학의 융합을 통해 재편되고 있습니다. 농가들은 토양 수분 프로브, 기상 관측소, 작물 조사 앱, 원격 감지 기술을 활용하여 밭별 변동을 파악하고, 투입 자재를 보다 정확하게 적용함으로써 사후 대응형 밭 관리에서 데이터 기반 의사 결정으로 전환해 가고 있습니다.
스마트 농업에서 인공지능이 미치는 누적 영향
인공지능(AI)은 스마트 농업의 '지능 계층'으로 자리 잡고 있습니다. AI 모델은 이미지 데이터를 바탕으로 작물의 스트레스 상태를 분류하거나, 해충 및 병해 발생을 예측하거나, 관개 일정을 제안하거나, 수확량을 추정하거나, 등급 평가를 자동화하거나, 농기계의 주행 경로를 최적화하는 데 활용되고 있습니다. 농장이 기후 변화, 노동력 부족, 투입 비용 상승과 같은 과제에 직면한 가운데, 이러한 응용 기술은 특히 그 가치가 높아지고 있습니다.
세계 스마트 농업 시장의 주요 지역별 분석
아시아태평양은 대규모 농업 인구, 세분화된 농장 구조, 물 부족 문제, 그리고 정부의 강력한 디지털화 프로그램이 맞물리면서 도입의 주요 전초기지가 되고 있습니다. 중국, 인도, 일본, 호주, 한국에서는 스마트 관개, 보호 재배, 로봇 공학, 농업용 드론, 위성을 활용한 작물 모니터링이 진행되고 있는 반면, 동남아시아 국가들에서는 디지털 자문 도구를 활용하여 벼, 팜유, 양식, 원예 분야의 생산성 향상을 지원하고 있습니다.
스마트 농업 도입을 주도하는 주요 그룹별 인사이트
아세안(ASEAN)은 회원국들이 쌀, 양식, 팜유, 원예 분야의 밸류체인을 디지털화하고 있어, 높은 잠재력을 지닌 스마트 농업 그룹으로 부상하고 있습니다. 도입은 대부분의 경우 서비스 주도로 이루어지고 있으며, 모바일 플랫폼, 위성 데이터를 통한 인사이트, 협동조합을 기반으로 한 기계화가 소규모 농가의 분산화, 통신 환경의 불균형, 자금 조달의 어려움과 같은 과제 극복에 기여하고 있습니다.
스마트 농업의 성장을 위한 주요국의 인사이트
미국은 정밀 농업, 자율형 장비의 시범 도입, 농업 분석, 그리고 디지털 기술을 활용한 줄 재배 작물 재배 시스템 분야에서 주도적인 역할을 수행하고 있습니다. 한편, 캐나다는 혹독한 기후 조건 하에서 곡물, 유지종자, 축산 및 제어된 환경에서의 생산에 스마트 농업을 적용하고 있습니다. 멕시코에서는 수출용 농산물을 대상으로 디지털 관개, 온실 자동화, 추적성 도구가 도입되고 있으며, 브라질에서는 위성 모니터링과 가변 시비법의 적극적인 도입에 힘입어 대두, 옥수수, 사탕수수, 소, 커피 등의 분야에서 정밀 농업이 지속적으로 확대되고 있습니다.
스마트 농업의 리더를 위한 실천적 제안
업계 리더는 상호 운용성, 측정 가능한 투자 수익률(ROI), 그리고 농가가 사용하기 쉬운 점을 우선시해야 합니다. 기계 데이터, 농업 기록, 기상 정보, 위성 이미지, 가축 정보, 관개 제어, 재무 워크플로우를 통합한 스마트 농업 플랫폼은 고립된 단일 기능 솔루션보다 우위를 차지합니다. 공급업체는 도입 장벽을 낮추기 위해 개방형 API, 안전한 데이터 교환, 그리고 기존 농기계와의 호환성을 지원해야 합니다.
조사 방법
본 요약 보고서는 시장 정보 수집의 모범 사례에 따라 체계적인 2차 조사 기법을 활용하여 작성되었습니다. 본 분석에서는 국제기구, 정부 농업 기관, 지속가능성 프레임워크, 업계 간행물, 기술 도입 관련 조사, 학술 문헌, 그리고 정밀 농업, 디지털 농업, 농업 분야의 AI, 스마트 관개, 로봇 공학, 커넥티드 팜 시스템과 관련된 공개 정보 및 널리 인정받는 정보원을 종합하고 있습니다.
결론
스마트 농업은 회복력이 뛰어나고 생산성이 높으며 지속 가능한 식량 시스템을 구축하기 위한 전략적 요건으로 자리 잡고 있습니다. AI, IoT, 원격 감지, 로봇 공학, 자동화, 스마트 관개 및 분석 기술을 결합함으로써, 생산자는 농지, 가축, 온실, 공급망의 각 단계에서 발생하는 변동성을 보다 정밀하게 관리할 수 있게 됩니다.
자주 묻는 질문
목차
제1장 서문
제2장 조사 방법
제3장 주요 요약
제4장 시장 개요
제5장 시장 인사이트
제6장 AI의 누적 영향(2026년년)
제7장 스마트 농업 시장 : 제공별
제8장 스마트 농업 시장 : 접속성별
제9장 스마트 농업 시장 : 기술별
제10장 스마트 농업 시장 : 도입 모드별
제11장 스마트 농업 시장 : 농장 규모별
제12장 스마트 농업 시장 : 사용자 유형별
제13장 스마트 농업 시장 : 용도별
제14장 스마트 농업 시장 : 지역별
제15장 스마트 농업 시장 : 그룹별
제16장 스마트 농업 시장 : 국가별
제17장 경쟁 구도
제18장 기업 개요
AJY 26.07.15The Smart Agriculture Market is projected to grow by USD 55.91 billion at a CAGR of 13.29% by 2032.
| KEY MARKET STATISTICS | |
|---|---|
| Base Year [2025] | USD 23.33 billion |
| Estimated Year [2026] | USD 26.29 billion |
| Forecast Year [2032] | USD 55.91 billion |
| CAGR (%) | 13.29% |
Smart agriculture is moving from a technology category to a core operating model for modern food systems. It combines precision agriculture, farm management software, IoT sensors, satellite imagery, variable-rate equipment, autonomous machinery, robotics, drones, connected irrigation, and advanced analytics to improve productivity, resource efficiency, traceability, and climate resilience.
The sector's urgency is grounded in measurable global pressures. The United Nations projects the world population to reach about 9.7 billion by 2050, while agriculture accounts for roughly 70% of global freshwater withdrawals, according to widely cited FAO and World Bank data. These constraints are pushing growers, agribusinesses, governments, and technology providers to adopt digital farming solutions that raise yields while reducing water, fertilizer, energy, and labor intensity.
Transformative Shifts in the Smart Agriculture Landscape
The smart agriculture landscape is being reshaped by the convergence of connected devices, cloud platforms, satellite-based monitoring, and digital agronomy. Growers are increasingly replacing reactive field management with data-driven decision-making, using soil moisture probes, weather stations, crop scouting applications, and remote sensing to identify variability across fields and apply inputs more precisely.
A second structural shift is the movement from standalone farm tools to integrated digital ecosystems. Equipment manufacturers, seed and crop input suppliers, irrigation providers, telecom operators, and software vendors are building interoperable platforms that connect machinery, agronomic data, finance, insurance, and supply chain reporting. This shift supports measurable gains in input optimization, regulatory compliance, carbon accounting, food traceability, and farm profitability.
Cumulative Impact of Artificial Intelligence in Smart Agriculture
Artificial intelligence is becoming the intelligence layer of smart agriculture. AI models are used to classify crop stress from imagery, predict pest and disease outbreaks, recommend irrigation schedules, estimate yield, automate grading, and optimize machinery routes. These applications are particularly valuable as farms face climate volatility, labor shortages, and rising input costs.
The cumulative impact of AI is not limited to yield improvement. Machine learning supports earlier intervention, lower chemical usage, improved water productivity, stronger livestock monitoring, and more reliable supply chain forecasting. As generative AI and decision-support tools mature, agronomists and farm managers can convert complex datasets into practical recommendations, making digital farming more accessible to both large-scale commercial farms and smaller producers connected through cooperatives or service providers.
Key Regional Insights Across Global Smart Agriculture Markets
Asia-Pacific is a major adoption frontier because it combines large agricultural populations, fragmented farm structures, water stress, and strong government digitalization programs. China, India, Japan, Australia, and South Korea are advancing smart irrigation, protected cultivation, robotics, agricultural drones, and satellite-enabled crop monitoring, while Southeast Asian economies are using digital advisory tools to support rice, palm, aquaculture, and horticulture productivity.
North America remains one of the most mature smart agriculture regions, supported by large farm sizes, advanced machinery penetration, GNSS-guided equipment, strong agritech investment, and established crop insurance and data infrastructure. Latin America is expanding precision farming across Brazil, Mexico, Argentina, and Chile, particularly in soybeans, corn, sugarcane, coffee, and fruit exports where productivity, water management, and traceability are commercially important.
Europe's adoption is shaped by sustainability regulation, the European Green Deal, Common Agricultural Policy incentives, and demand for reduced pesticide and fertilizer intensity. The Middle East is prioritizing controlled-environment agriculture, desalination-linked irrigation, greenhouse production, and water-efficient farming due to arid conditions, while Africa is adopting mobile advisory, satellite weather intelligence, digital finance, and low-cost sensor models to improve food security and smallholder resilience.
Key Group Insights Shaping Smart Agriculture Adoption
ASEAN is emerging as a high-potential smart agriculture group as member states digitize rice, aquaculture, palm oil, and horticulture value chains. Adoption is often service-led, with mobile platforms, satellite insights, and cooperative-based mechanization helping overcome smallholder fragmentation, uneven connectivity, and limited capital access.
The GCC is focused on food security, water conservation, greenhouse farming, vertical farming, and AI-enabled irrigation, reflecting the region's dependence on food imports and scarce arable land. The European Union is advancing precision agriculture through sustainability policy, data governance, and funding for climate-smart farming, making compliance analytics, soil monitoring, and input-reduction technologies especially relevant.
BRICS economies represent both scale and diversity, spanning large grain systems, tropical crops, livestock, and smallholder agriculture across Brazil, Russia, India, China, and South Africa. G7 countries are driving high-value innovation in robotics, autonomous equipment, digital twins, agricultural AI, and climate-smart farm management, while NATO members increasingly view resilient food systems, secure satellite data, and cyber-protected agricultural infrastructure as part of broader strategic resilience.
Key Country Insights for Smart Agriculture Growth
The United States leads in precision farming, autonomous equipment trials, agronomic analytics, and digitally enabled row-crop systems, while Canada applies smart agriculture to grain, oilseed, livestock, and controlled-environment production under challenging climate conditions. Mexico is adopting digital irrigation, greenhouse automation, and traceability tools for export-oriented produce, and Brazil continues to scale precision agriculture across soybeans, corn, sugarcane, cattle, and coffee, supported by strong adoption of satellite monitoring and variable-rate practices.
In Europe, the United Kingdom emphasizes agri-robotics, livestock monitoring, and farm data platforms; Germany combines advanced machinery engineering with digital farm management; France is investing in agroecology-aligned precision tools; Italy and Spain are expanding smart irrigation, specialty crop monitoring, and protected cultivation; and Russia uses digital farming across large grain operations where remote sensing, yield mapping, and machinery optimization are important.
China is a major force in agricultural drones, digital villages, smart livestock, and AI-enabled farm monitoring, while India's opportunity centers on digital advisory, weather intelligence, soil health tools, and scalable services for smallholders. Japan and South Korea are advancing robotics, smart greenhouses, and automation to address aging farm labor, and Australia is using satellite analytics, water management, autonomous systems, and livestock monitoring for extensive broadacre and pastoral operations.
Actionable Recommendations for Smart Agriculture Leaders
Industry leaders should prioritize interoperability, measurable return on investment, and farmer usability. Smart agriculture platforms that integrate machinery data, agronomic records, weather intelligence, satellite imagery, livestock information, irrigation controls, and financial workflows are better positioned than isolated point solutions. Vendors should support open APIs, secure data exchange, and compatibility with existing farm equipment to reduce adoption friction.
Decision-makers should also align product strategies with regional constraints. In water-stressed markets, irrigation intelligence, soil moisture analytics, and evapotranspiration modeling should be central. In labor-constrained markets, robotics and automation can deliver stronger value. In smallholder-dominated markets, subscription, cooperative, and service-provider models can improve affordability and scale. Across all markets, cybersecurity, data ownership transparency, agronomic validation, and evidence-based yield or input savings claims are essential for trust.
Research Methodology
This executive summary is developed using a structured secondary research methodology consistent with market intelligence best practices. The analysis synthesizes publicly available and widely recognized sources, including international organizations, government agriculture agencies, sustainability frameworks, trade publications, technology adoption studies, academic literature, and public disclosures related to precision agriculture, digital farming, AI in agriculture, smart irrigation, robotics, and connected farm systems.
Insights are validated through triangulation across demand drivers, technology maturity, regional policy signals, agricultural resource constraints, digital infrastructure, and commercially observable adoption patterns. The methodology prioritizes data-backed indicators such as population growth, freshwater withdrawals, climate risk, farm labor trends, connectivity, national food security strategies, and sustainable agriculture policies while avoiding unsupported market-size, market-share, and forecasting claims.
Conclusion
Smart agriculture is becoming a strategic requirement for resilient, productive, and sustainable food systems. The combination of AI, IoT, remote sensing, robotics, automation, smart irrigation, and analytics allows producers to manage variability at field, animal, greenhouse, and supply chain levels with greater precision.
As climate volatility, water scarcity, labor shortages, and food security concerns intensify, the strongest opportunities will accrue to organizations that deliver practical, interoperable, and economically proven solutions. The next phase of smart agriculture adoption will be defined by trusted data ecosystems, localized agronomic intelligence, and technologies that convert sustainability goals into measurable farm-level outcomes.
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. Smart Agriculture Market, by Offering
- 7.1. Hardware
- 7.1.1. Automation & Control Systems
- 7.1.1.1. Application Control Devices
- 7.1.1.2. Autonomous Tractor
- 7.1.1.3. Drones
- 7.1.1.4. Guidance System
- 7.1.1.5. Wireless Modules
- 7.1.2. HVAC System
- 7.1.3. RFID Tags & Readers
- 7.1.4. Sensors
- 7.1.4.1. Climate Sensors
- 7.1.4.2. Soil Sensor
- 7.1.4.3. Water Sensor
- 7.1.1. Automation & Control Systems
- 7.2. Services
- 7.2.1. Managed Services
- 7.2.1.1. Maintenance & Support Services
- 7.2.1.2. Training & Consulting Services
- 7.2.2. Professional Services
- 7.2.2.1. Climate Information Services
- 7.2.2.2. Supply Chain Management Services
- 7.2.1. Managed Services
- 7.3. Software
- 7.3.1. Data Analytics Platforms
- 7.3.2. Farm Management Software
8. Smart Agriculture Market, by Connectivity
- 8.1. Cellular
- 8.1.1. 3G
- 8.1.2. 4G
- 8.1.3. 5G
- 8.2. LPWAN
- 8.3. Satellite
- 8.4. Wireless Sensor Network
- 8.4.1. Bluetooth
- 8.4.2. Wi-Fi
- 8.4.3. ZigBee
9. Smart Agriculture Market, by Technology
- 9.1. Artificial Intelligence & Machine Learning
- 9.2. Cloud Computing
- 9.3. Internet of Things
- 9.4. Robotics and Automation
10. Smart Agriculture Market, by Deployment Mode
- 10.1. On Premise
- 10.2. On-Cloud
11. Smart Agriculture Market, by Farm Size
- 11.1. Cooperative Farm
- 11.2. Large Commercial Farm
- 11.3. Smallholder Farm
12. Smart Agriculture Market, by User Type
- 12.1. Agronomists
- 12.2. Farmers
- 12.3. Government Agencies
13. Smart Agriculture Market, by Application
- 13.1. Greenhouse Management
- 13.1.1. Climate Control
- 13.1.2. Pest Management
- 13.2. Irrigation Management
- 13.2.1. Drip Irrigation
- 13.2.2. Sprinkler Irrigation
- 13.3. Livestock Monitoring
- 13.3.1. Animal Health Monitoring
- 13.3.2. Behavior Monitoring
- 13.4. Precision Farming
- 13.5. Soil Monitoring
- 13.5.1. Nutrient Monitoring
- 13.5.2. Texture Monitoring
14. Smart Agriculture Market, by Region
- 14.1. Asia-Pacific
- 14.2. North America
- 14.3. Latin America
- 14.4. Europe
- 14.5. Middle East
- 14.6. Africa
15. Smart Agriculture Market, by Group
- 15.1. ASEAN
- 15.2. GCC
- 15.3. European Union
- 15.4. BRICS
- 15.5. G7
- 15.6. NATO
16. Smart Agriculture 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 Concentration Analysis, 2025
- 17.1.1. Concentration Ratio (CR)
- 17.1.2. Herfindahl Hirschman Index (HHI)
- 17.2. Recent Developments & Impact Analysis, 2025
- 17.3. Product Portfolio Analysis, 2025
- 17.4. Benchmarking Analysis, 2025
18. Company Profiles
- 18.1. AgCinect LLC
- 18.2. AGCO Corporation
- 18.3. AgEagle Aerial Systems Inc.
- 18.4. AgriDigital
- 18.5. AgriProve Pty Ltd.
- 18.6. Agrivi d.o.o.
- 18.7. AGROBOT
- 18.8. Bunge Limited
- 18.9. Cargill, Incorporated
- 18.10. CNH Industrial NV
- 18.11. Corteva Agriscience, Inc.
- 18.12. Cropin Technology Solutions Pvt. Ltd.
- 18.13. CropZilla Inc.
- 18.14. Deere & Company
- 18.15. Eka Software Solutions
- 18.16. Equinox's Drones Pvt. Ltd.
- 18.17. Fancom B.V.
- 18.18. Farmers Edge Inc.
- 18.19. GEA Group Aktiengesellschaft
- 18.20. GrainChain, Inc.
- 18.21. Hexagon AB
- 18.22. Indigo Ag, Inc.
- 18.23. International Business Machines Corporation
- 18.24. Jingxun Changtong Electronic Technology Co., Ltd.
- 18.25. Lely International N.V.
- 18.26. LexisNexis Risk Solutions
- 18.27. Microsoft Corporation
- 18.28. Nelson Irrigation Corporation
- 18.29. Oracle Corporation
- 18.30. SAP SE
- 18.31. SemiosBio Technologies Inc.
- 18.32. Syngenta AG
- 18.33. SZ DJI Technology Co., Ltd.
- 18.34. Topcon Positioning Systems, Inc.
- 18.35. Trimble Inc.
