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시장보고서
상품코드
2083535
스마트 농업 분야 IoT 시장 : 제공 형태, 접속성, 농장 유형, 통합 레벨, 용도, 도입 아키텍처별 - 세계 시장 예측(2026-2032년)IoT in Smart Farming Market by Offering, Connectivity, Farm Type, Integration Level, Application, Deployment Architecture - Global Forecast 2026-2032 |
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360iResearch
스마트 농업 IoT 시장은 2032년까지 연평균 복합 성장률(CAGR) 8.59%로 성장해 162억 달러 규모로 확대될 것으로 예측됩니다.
| 주요 시장 통계 | |
|---|---|
| 기준 연도(2025년) | 90억 9,000만 달러 |
| 추정 연도(2026년) | 98억 5,000만 달러 |
| 예측 연도(2032년) | 162억 달러 |
| CAGR(%) | 8.59% |
스마트 농업 분야 IoT는 실험적인 시범 단계에서 현대 농업 운영의 핵심 단계로 점차 전환되고 있습니다. 네트워크에 연결된 토양 센서, 기상 관측소, 가축용 웨어러블 기기, GPS가 탑재된 농업 기계, 관개 제어 장치, 드론 및 농장 관리 플랫폼은 생산자가 밭의 상황을 거의 실시간으로 측정하고, 그 정보를 운영상의 의사 결정에 반영하는 데 도움이 되고 있습니다.
이 비즈니스 사례는 식량 수요 증가, 기후 변화, 물 부족, 노동력 부족, 투입 자재의 효율화 필요성 등 구조적인 압박 요인들로 인해 그 타당성이 더욱 뒷받침되고 있습니다. FAO, USDA, OECD, 유로스타트, 세계은행 및 각국 농림부가 제공하는 검증된 데이터 세트는 농업이 환경에 미치는 영향을 줄이면서, 더 적은 천연 자원으로 더 많은 생산을 이루어내야 한다는 점을 일관되게 보여주고 있습니다. 그 결과, 정밀 농업, 디지털 농업 및 농업용 IoT 시스템은 단순한 선택적 기술 업그레이드가 아니라, 생산성, 지속가능성 및 위험 관리를 위한 인프라로 점점 더 인식되고 있습니다.
스마트 농업 현장에서 일어나는 혁신적인 변화
스마트 농업의 양상은 저비용 센서, 클라우드 연결, 엣지 컴퓨팅, 위성 이미지, 가변 시비 장비, 로봇 공학, 상호 운용 가능한 농업 데이터 플랫폼의 융합을 통해 재편되고 있습니다. 이러한 기술 덕분에 농업은 ‘달력에 기반한 의사결정’에서 ‘상황에 따른 운영’ 및 ‘예측에 기반한 운영’으로 전환되고 있습니다.
농업용 IoT에 대한 인공지능의 누적 영향
인공지능은 지속적으로 수집되는 농장 데이터를 권장 사항, 예측 및 자동화된 조치로 변환함으로써 스마트 농업 분야에서 IoT의 가치를 한층 더 높이고 있습니다. 머신러닝 모델은 센서 데이터, 위성 이미지, 드론 이미지, 수확량 지도, 기상 기록 및 과거 농업 데이터를 분석하여 작물의 스트레스를 감지하고, 질병 위험을 추정하며, 관개 시기를 최적화하고, 가변율 비료 시비를 유도하며, 수확량을 예측할 수 있습니다.
주요 지역별 분석 : 아시아태평양, 북미, 라틴아메리카, 유럽, 중동 및 아프리카
아시아태평양은 대규모 농업 인력, 다양한 작물 재배 체계, 기계화의 진전, 그리고 디지털 농업에 대한 국민들의 지지가 높아지는 등 여러 요인이 맞물려 스마트 농업 분야에서 IoT의 잠재력이 매우 높은 지역입니다. 중국, 인도, 일본, 호주, 한국에서는 센서를 활용한 관개, 위성 모니터링, 로봇 기술, 보호 재배, 가축 추적, 그리고 정부 주도의 현대화 프로그램을 통해 스마트 농업이 추진되고 있습니다. 도입 현황은 지역에 따라 크게 다르며, 일본과 한국에서는 고도화된 자동화가 진행되고 있고, 중국과 인도에서는 대규모 디지털 농업 이니셔티브가 전개되고 있으며, 호주에서는 기후 변화에 강한 정밀 농업이 실천되고 있습니다.
주요 지역별 인사이트 : 아세안(ASEAN), GCC, 유럽연합(EU), 브릭스(BRICS), G7, 나토(NATO)
아세안(ASEAN) 국가들에서는 벼 재배 모니터링, 수산 양식용 센서, 농장 관리, 기상 정보를 기반으로 한 자문 도구, 모바일 우선형 디지털 농업 플랫폼 등을 통해 스마트 농업 분야 IoT 도입이 진행되고 있습니다. 정부나 농업 관련 기업들이 스마트 농업을 식량 안보, 수출 품질, 기후 변화에 대한 내성, 소규모 농가의 생산성 향상과 연계하고 있는 지역에서 그 도입이 가장 활발히 이루어지고 있습니다. GCC 지역에서는 담수 자원이 부족하기 때문에 센서를 활용한 관개, 온실 자동화, 비료 및 관개 제어, 해수 담수화와 연계된 농업, 그리고 제어된 환경에서의 농업이 각국의 식량 안보 전략의 핵심을 이루고 있습니다.
주요 스마트 농업 분야 IoT 시장의 국가별 주요 동향
미국은 강력한 농업 기업, 농업 기계, 농업 기술 보급 및 디지털 농업 생태계의 뒷받침을 받아, 사물인터넷(IoT) 기기, 수확량 매핑, 가변 시비, 정밀 관개, 데이터 기반 농장 관리 분야에서 세계를 선도하고 있습니다. 캐나다에서는 광활한 영토와 변화무쌍한 기상 조건 속에서 곡물, 유지종자, 유제품, 축산 분야의 생산성을 높이기 위해 IoT를 활용하고 있습니다. 한편, 멕시코에서의 IoT 도입은 보호재배, 관개 관리, 그리고 수출용 작물의 품질 향상과 관련이 있습니다. 브라질은 특히 대두, 옥수수, 사탕수수, 커피 및 축산 분야에서 IoT를 활용한 대규모 농업의 주요 원동력이 되고 있으며, 강력한 농업 비즈니스 물류 체계와 원격 모니터링에 대한 수요가 추가적인 디지털화 도입을 뒷받침하고 있습니다.
스마트 농업의 리더를 위한 실천적 제안
업계 리더는 상호 운용성, 농업적 타당성, 사이버 보안, 그리고 측정 가능한 투자 수익률(ROI)을 우선시해야 합니다. 기기 데이터, 농장 센서, 위성 이미지, 기상 정보, 가축 데이터 및 기업 시스템을 통합하는 IoT 플랫폼은 단편적인 기기 도입보다 더 뛰어난 성과를 가져옵니다. 벤더는 오프라인 기능, 열악한 농장 환경, 저전력 연결성, 개방형 데이터 표준, 그리고 농장 업무 흐름에 적합한 간편한 사용자 경험을 고려하여 설계해야 합니다.
조사 방법
본 요약본은 검증된 공개 정보 및 기관 정보원을 활용한 2차 조사, 데이터 삼각측량, 그리고 업계 분석을 바탕으로 작성되었습니다. 주요 참고 자료로는 농업 통계, 연결성 지표, 기후·수자원 데이터, 기술 도입에 관한 조사, 정책 문서, 그리고 FAO, 세계은행, OECD, 미국 농무부(USDA), 유로스타트, 각국 통계청 및 정부 기관이 발표한 각국의 스마트 농업 이니셔티브 등이 포함됩니다.
결론
스마트 농업에서 IoT는 회복력이 뛰어나고 효율적이며 지속 가능한 농업을 위한 전략적 기반이 되어가고 있습니다. 가장 큰 기회는 연결된 기기, AI 분석, 자동화 및 자문 서비스가 물 사용 최적화, 투입 자재 효율화, 노동 생산성, 가축 건강, 기후 위험 관리와 같은 측정 가능한 농업적 과제를 해결하는 실용적인 워크플로우로 통합되는 분야에서 발생하고 있습니다.
자주 묻는 질문
목차
제1장 서문
제2장 조사 방법
제3장 주요 요약
제4장 시장 개요
제5장 시장 인사이트
제6장 AI의 누적 영향(2026년)
제7장 스마트 농업 분야 IoT 시장 : 제공별
제8장 스마트 농업 분야 IoT 시장 : 접속성별
제9장 스마트 농업 분야 IoT 시장 : 농장 유형별
제10장 스마트 농업 분야 IoT 시장 : 통합 레벨별
제11장 스마트 농업 분야 IoT 시장 : 용도별
제12장 스마트 농업 분야 IoT 시장 : 도입 아키텍처별
제13장 스마트 농업 분야 IoT 시장 : 지역별
제14장 스마트 농업 분야 IoT 시장 : 그룹별
제15장 스마트 농업 분야 IoT 시장 : 국가별
제16장 경쟁 구도
제17장 기업 개요
KTH 26.07.15The IoT in Smart Farming Market is projected to grow by USD 16.20 billion at a CAGR of 8.59% by 2032.
| KEY MARKET STATISTICS | |
|---|---|
| Base Year [2025] | USD 9.09 billion |
| Estimated Year [2026] | USD 9.85 billion |
| Forecast Year [2032] | USD 16.20 billion |
| CAGR (%) | 8.59% |
IoT in smart farming is moving from experimental pilots to a core layer of modern agricultural operations. Connected soil sensors, weather stations, livestock wearables, GPS-enabled machinery, irrigation controllers, drones, and farm management platforms help producers measure field conditions in near real time and convert those signals into operational decisions.
The business case is reinforced by structural pressures: rising food demand, climate volatility, water scarcity, labor constraints, and the need to improve input efficiency. Verified datasets from FAO, USDA, OECD, Eurostat, World Bank, and national agriculture ministries consistently show that agriculture must produce more with fewer natural resources while reducing environmental impact. As a result, precision agriculture, digital agriculture, and farm IoT systems are increasingly viewed as productivity, sustainability, and risk-management infrastructure rather than optional technology upgrades.
Transformative Shifts in the Smart Farming Landscape
The smart farming landscape is being reshaped by the convergence of low-cost sensors, cloud connectivity, edge computing, satellite imagery, variable-rate equipment, robotics, and interoperable farm data platforms. These technologies are shifting agriculture from calendar-based decision-making to condition-based and predictive operations.
A major transformation is the move from standalone farm devices to connected ecosystems. Irrigation systems can respond to soil moisture and weather forecasts, livestock monitoring tools can flag health anomalies earlier, and machinery telematics can support preventive maintenance, route planning, and fuel optimization. The next competitive frontier is not merely device adoption, but the ability to integrate agronomic, equipment, weather, market, traceability, and sustainability data into a unified decision workflow.
Cumulative Impact of Artificial Intelligence on Farm IoT
Artificial intelligence is magnifying the value of IoT in smart farming by turning continuous field data into recommendations, forecasts, and automated actions. Machine learning models can analyze sensor streams, satellite images, drone imagery, yield maps, weather records, and historical farm data to detect crop stress, estimate disease risk, optimize irrigation timing, guide variable-rate input application, and forecast yield outcomes.
The cumulative impact is visible across the farm value chain. AI-enabled IoT supports precision input application, early pest and disease detection, automated grading and sorting, robotics guidance, livestock welfare monitoring, and predictive maintenance for connected machinery. However, the impact depends on data quality, connectivity, model transparency, cybersecurity, and agronomic validation. Leaders should prioritize AI solutions that are explainable, field-tested, and aligned with local soil, climate, crop, and livestock conditions.
Key Regional Insights: Asia-Pacific, North America, Latin America, Europe, Middle East, and Africa
Asia-Pacific is a high-potential region for IoT in smart farming because it combines large agricultural workforces, diverse crop systems, rising mechanization, and growing public support for digital agriculture. China, India, Japan, Australia, and South Korea are advancing smart farming through sensor-based irrigation, satellite monitoring, robotics, protected cultivation, livestock tracking, and government-backed modernization programs. Adoption is highly varied, with advanced automation in Japan and South Korea, large-scale digital agriculture initiatives in China and India, and climate-resilient precision farming in Australia.
North America remains one of the most mature regions for precision agriculture and connected farm equipment, supported by large farm operations, established machinery ecosystems, university extension networks, broadband initiatives, and extensive agronomic service capabilities. Latin America, led by Brazil and Mexico, is adopting IoT to improve productivity across row crops, livestock, irrigation-intensive agriculture, and export-oriented agribusiness, though rural connectivity, financing, and technical support remain important constraints.
Europe benefits from policy-driven sustainability requirements, advanced agricultural engineering, digital traceability priorities, and strong research ecosystems, especially across the European Union. The Middle East is focusing on water-efficient controlled-environment agriculture, greenhouse automation, desalination-linked food production, and sensor-led irrigation due to chronic water scarcity. Africa's opportunity is concentrated in mobile-enabled advisory services, climate-smart agriculture, remote sensing, low-power IoT, and scalable models that address smallholder needs across fragmented production systems.
Key Group Insights: ASEAN, GCC, European Union, BRICS, G7, and NATO
ASEAN markets are advancing IoT in smart farming through rice monitoring, aquaculture sensors, plantation management, weather-based advisory tools, and mobile-first digital agriculture platforms. Adoption is strongest where governments and agribusinesses connect smart farming with food security, export quality, climate resilience, and smallholder productivity. In the GCC, scarce freshwater resources make sensor-based irrigation, greenhouse automation, fertigation control, desalination-linked agriculture, and controlled-environment farming central to national food security strategies.
The European Union is a policy-led innovation hub where precision agriculture aligns with sustainability, traceability, nutrient management, biodiversity objectives, and emissions reduction goals. BRICS economies bring scale and diversity: Brazil, China, India, Russia, and South Africa each have distinct priorities ranging from large-scale commodity production and satellite analytics to smallholder digitization, irrigation efficiency, and climate adaptation. G7 countries are shaping high-value automation, farm robotics, AI governance, cybersecurity, rural connectivity, and data interoperability, while NATO members overlap with markets investing in resilient food systems, secure digital infrastructure, and supply chain continuity.
Key Country Insights Across Major Smart Farming Markets
The United States leads in connected equipment, yield mapping, variable-rate application, precision irrigation, and data-driven farm management, supported by strong agribusiness, machinery, extension, and digital agriculture ecosystems. Canada is using IoT to improve grain, oilseed, dairy, and livestock productivity across vast geographies and variable weather conditions, while Mexico's adoption is linked to protected agriculture, irrigation management, and export crop quality. Brazil is a major engine for IoT-enabled large-scale farming, especially in soybeans, corn, sugarcane, coffee, and cattle operations, while strong agribusiness logistics and remote monitoring needs support further digital adoption.
In Europe, the United Kingdom, Germany, France, Italy, and Spain are advancing precision farming through robotics, sensors, machinery telematics, water management, nutrient efficiency, and sustainability compliance. Germany and France benefit from strong agricultural engineering and research capabilities, Italy and Spain emphasize high-value crops and irrigation efficiency, and the United Kingdom is accelerating farm automation and data-led productivity initiatives. Russia's large land base creates opportunities for remote monitoring, satellite analytics, fleet management, and connected equipment across broadacre systems.
In Asia-Pacific, China and India are critical scale markets with strong government interest in digital agriculture, remote sensing, mechanization, and farm advisory systems, though adoption varies widely by farm size, connectivity, and regional infrastructure. Japan and South Korea emphasize automation, robotics, greenhouse control, smart livestock systems, and high-efficiency farming to address labor shortages and limited arable land. Australia uses IoT for drought response, remote farm operations, livestock tracking, water monitoring, and broadacre precision agriculture, while its research networks and producer groups support climate-resilient adoption.
Actionable Recommendations for Smart Farming Leaders
Industry leaders should prioritize interoperability, agronomic relevance, cybersecurity, and measurable return on investment. IoT platforms that integrate equipment data, field sensors, satellite imagery, weather intelligence, livestock data, and enterprise systems will outperform fragmented device deployments. Vendors should design for offline functionality, rugged farm environments, low-power connectivity, open data standards, and simple user experiences suited to farm workflows.
Farm operators should start with high-value use cases such as irrigation optimization, input reduction, livestock health alerts, fuel and equipment monitoring, pest and disease surveillance, and yield variability analysis. Agribusinesses and policymakers should expand rural connectivity, support digital skills, create trusted data-sharing frameworks, promote climate-smart agriculture, and encourage financing models that reduce upfront adoption barriers for small, medium, and large producers.
Research Methodology
This executive summary is based on secondary research, data triangulation, and industry analysis using verified public and institutional sources. Core reference points include agricultural statistics, connectivity indicators, climate and water data, technology adoption studies, policy documents, and national smart agriculture initiatives from FAO, World Bank, OECD, USDA, Eurostat, national statistical offices, and government agencies.
The methodology emphasizes cross-validation across multiple datasets and sources to avoid reliance on a single indicator. Regional, group, and country insights are assessed through the lenses of farm structure, crop mix, connectivity readiness, policy support, water stress, labor availability, equipment penetration, digital agriculture maturity, sustainability requirements, and the practical readiness of IoT-enabled farming workflows.
Conclusion
IoT in smart farming is becoming a strategic foundation for resilient, efficient, and sustainable agriculture. The strongest opportunities are emerging where connected devices, AI analytics, automation, and advisory services are integrated into practical workflows that solve measurable farm problems such as water optimization, input efficiency, labor productivity, animal health, and climate risk management.
As climate risks intensify and food systems face pressure to improve productivity, traceability, and resource efficiency, stakeholders that combine reliable technology with local agronomic expertise, secure data governance, and farmer-centric implementation will be best positioned to lead the next phase of precision agriculture and digital farming.
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
- 4.3.1.1. Productivity pressure turns real-time farm data into an operational necessity
- 4.3.1.2. Resource efficiency requirements accelerate precision irrigation and input optimization
- 4.3.1.3. Labor scarcity advances the business case for automation and remote monitoring
- 4.3.1.4. Hybrid connectivity expands the addressable farm base beyond well-connected regions
- 4.3.2. Key Restraints
- 4.3.2.1. Capital intensity and uncertain payback slow adoption outside large farms
- 4.3.2.2. Connectivity gaps and data silos limit reliable field-scale deployment
- 4.3.3. Key Opportunities
- 4.3.3.1. Retrofit and mixed-fleet solutions unlock underpenetrated mid-market demand
- 4.3.3.2. Water intelligence creates high-ROI revenue in drought-exposed regions
- 4.3.3.3. Verified farm data opens revenue beyond equipment and agronomy
- 4.3.4. Key Challenges
- 4.3.4.1. Mixed-fleet interoperability determines whether adoption scales beyond premium farms
- 4.3.4.2. Cybersecurity and lifecycle support become mandatory trust requirements
- 4.3.1. Key Drivers
- 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. IoT in Smart Farming Market, by Offering
- 7.1. Hardware
- 7.1.1. Controllers
- 7.1.2. Cameras
- 7.1.3. Positioning & Guidance Systems
- 7.1.4. Animal Wearables
- 7.1.4.1. Ear Tags
- 7.1.4.2. Collars
- 7.1.4.3. Leg Bands
- 7.1.5. Sensors
- 7.1.5.1. Temperature
- 7.1.5.2. Humidity
- 7.1.5.3. Soil
- 7.1.5.4. Pressure
- 7.1.5.5. Flow Monitoring
- 7.1.6. Connectivity & Edge Hardware
- 7.1.6.1. Gateways
- 7.1.6.2. Routers
- 7.1.6.3. Data Loggers
- 7.2. Solutions
- 7.2.1. Analytics
- 7.2.2. Device & Data Management
- 7.2.3. Farm Management Platform
- 7.3. Services
- 7.3.1. Consulting
- 7.3.2. Integration
- 7.3.3. Maintenance & Support
8. IoT in Smart Farming Market, by Connectivity
- 8.1. Bluetooth
- 8.2. Cellular
- 8.3. LPWAN
- 8.4. Satellite
- 8.5. Wi-Fi
- 8.6. Wired
9. IoT in Smart Farming Market, by Farm Type
- 9.1. Crop Farming
- 9.1.1. Cereal Crops
- 9.1.2. Horticulture
- 9.2. Aquaculture
- 9.3. Livestock Farming
- 9.3.1. Dairy
- 9.3.2. Poultry
- 9.3.3. Swine
10. IoT in Smart Farming Market, by Integration Level
- 10.1. Standalone Devices
- 10.2. Connected Device Bundles
- 10.3. Integrated Farm Platforms
11. IoT in Smart Farming Market, by Application
- 11.1. Crop Monitoring
- 11.1.1. Disease Detection
- 11.1.2. Yield Prediction
- 11.2. Controlled Environment Agriculture
- 11.3. Livestock Monitoring
- 11.3.1. Health Monitoring
- 11.3.2. Location Tracking
- 11.4. Precision Irrigation
- 11.5. Post-Harvest & Supply Chain Management
12. IoT in Smart Farming Market, by Deployment Architecture
- 12.1. On-premises
- 12.2. Cloud-hosted
- 12.3. Hybrid
13. IoT in Smart Farming 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. IoT in Smart Farming Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
15. IoT in Smart Farming 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. Deere & Company
- 17.2. Trimble Inc.
- 17.3. AGCO Corporation
- 17.4. CNH Industrial N.V.
- 17.5. Bayer AG
- 17.6. Valmont Industries, Inc.
- 17.7. Topcon Corporation
- 17.8. Kubota Corporation
- 17.9. Orbia Advance Corporation, S.A.B. de C.V.
- 17.10. DeLaval Holding AB
- 17.11. Syngenta Crop Protection AG
- 17.12. GEA Group Aktiengesellschaft
- 17.13. Hexagon AB
- 17.14. Corteva Agriscience
- 17.15. Microsoft Corporation
- 17.16. Semios
- 17.17. AKVA group ASA
- 17.18. HORSCH Maschinen SE & Co. KG
- 17.19. Yanmar Holdings Co., Ltd.
- 17.20. Nedap N.V.
- 17.21. TELUS Corporation
- 17.22. International Business Machines Corporation
- 17.23. Cisco Systems, Inc.
- 17.24. CropX Technologies Ltd.
- 17.25. Oracle Corporation
- 17.26. Ecorobotix SA
- 17.27. Amazon Web Services, Inc.
- 17.28. Arable Labs, Inc.
- 17.29. Gamaya
- 17.30. GAO Tek Inc.
- 17.31. Innovasea
- 17.32. KaaIoT Technologies, LLC
- 17.33. Libelium Comunicaciones Distribuidas SL
- 17.34. Smarter Technologies Group
- 17.35. Teralytic Holdings, Inc.
