시장보고서
상품코드
1986929

농업용 인공지능(AI) 시장 분석 및 예측(-2035년) : 유형, 제품, 서비스, 기술, 컴포넌트, 용도, 프로세스, 전개, 최종사용자, 솔루션별

Artificial Intelligence in Agriculture Market Analysis and Forecast to 2035: Type, Product, Services, Technology, Component, Application, Process, Deployment, End User, Solutions

발행일: | 리서치사: 구분자 Global Insight Services | 페이지 정보: 영문 350 Pages | 배송안내 : 3-5일 (영업일 기준)

    
    
    



※ 본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문 목차를 참고해주시기 바랍니다.

세계의 농업용 인공지능(AI) 시장은 2025년 39억 달러에서 2035년까지 95억 달러로 성장하여 CAGR 9.6%를 보일 것으로 예측됩니다. 이러한 성장은 정밀 농업의 도입 확대, 작물 모니터링 기능의 고도화에 대한 수요, 자원 활용과 수확량 최적화를 위한 AI 기술의 통합에 의해 주도되고 있습니다. 농업 분야 AI 시장은 적당히 통합된 구조를 특징으로 하며, 주요 부문은 정밀농업(시장 점유율 약 45%), 가축 모니터링(30%), 드론 분석(25%)으로 구성되어 있습니다. 주요 용도에는 작물 모니터링, 토양 관리, 예측 분석 등이 포함되며, 이는 농업 분야에서의 AI 기술 도입을 주도하고 있습니다. 시장에서는 특히 기술 인프라가 잘 갖춰져 있고 이러한 발전을 뒷받침하는 선진 지역에서 AI를 활용한 기기 및 소프트웨어 솔루션의 도입이 증가하고 있습니다.

경쟁 환경은 세계 기업과 지역 기업이 혼재되어 있으며, 기술 대기업과 전문 농업 기술 기업이 큰 역할을 하고 있습니다. 머신러닝 알고리즘과 데이터 분석 능력의 지속적인 발전으로 혁신의 정도는 높은 수준에 이르렀습니다. 기술력 및 시장 도달 범위를 확대하기 위한 인수합병과 전략적 제휴가 두드러지게 나타나고 있습니다. 기업들은 제품 라인업을 강화하고 시장 침투율을 높이기 위해 연구기관 및 농업협동조합과의 협력을 강화하고 있습니다. 이러한 역동적인 환경은 경쟁과 협력을 함께 촉진하며 시장을 주도하고 있습니다.

농업용 AI 시장의 '유형' 부문은 주로 머신러닝과 컴퓨터 비전 기술에 의해 주도되고 있습니다. 이는 작물 모니터링이나 예측 분석과 같은 복잡한 작업을 자동화하는 데 필수적인 기술입니다. 머신러닝은 방대한 양의 데이터를 처리하고 실행 가능한 인사이트를 창출할 수 있는 능력으로 의사결정 과정을 강화하기 위해 주류로 자리 잡고 있습니다. 컴퓨터 비전은 정밀 농업과 자율 주행 장비에 적용되면서 주목받고 있습니다. 농업 분야에서의 IoT 기기 도입 확대도 이 부문의 성장을 더욱 촉진하고 있습니다.

농업 AI의 기술은 예측 분석과 로봇공학이 주도하고 있습니다. 예측 분석은 작물 수확량을 예측하고 자원 배분을 최적화하여 효율성과 지속가능성을 향상시키는 데 필수적입니다. 로봇공학은 특히 수확과 심기 작업에서 인력 부족 해소와 생산성 향상에 기여하고 있습니다. AI와 IoT, 클라우드 컴퓨팅의 통합은 주목할 만한 추세로, 현대 농업에 필수적인 실시간 데이터 처리와 원격 모니터링을 가능하게 하고 있습니다.

응용 분야에서는 정밀 농업과 가축 모니터링이 주류가 되고 있습니다. 정밀농업은 AI를 활용하여 작물 재배에 있어 밭 단위의 관리를 최적화하고, 수확량 증가와 폐기물 감소로 이어집니다. 가축 모니터링은 AI를 활용해 건강 상태 모니터링과 행동 분석을 통해 동물 복지와 농장의 수익성을 향상시킵니다. 지속 가능한 농업 관행에 대한 수요 증가와 세계 식량 안보 문제에 대한 대응 필요성이 이 분야의 주요 촉진요인으로 작용하고 있습니다.

최종 사용자 분석에 따르면, 첨단 솔루션에 대한 투자 능력과 사업 규모에 따라 대규모 상업용 농장이 AI 기술을 도입하는 주요 주체가 되고 있습니다. 그러나 비용 절감과 기술의 이점이 명확해지면서 중소형 농장에서도 AI 도구의 도입이 확대되고 있습니다. 농업 현대화를 위한 정부의 이니셔티브와 보조금도 모든 규모의 농장에서의 도입을 촉진하고 있습니다.

컴포넌트 분야에서는 데이터 관리, 분석, 의사결정 지원을 위한 플랫폼을 제공하는 소프트웨어 솔루션이 시장을 주도하고 있습니다. 센서와 드론과 같은 하드웨어 구성 요소도 데이터 수집과 실시간 모니터링을 가능하게 하는 데 필수적입니다. AI 소프트웨어와 기존 농기계와의 통합이 확대되는 추세로, 기존 농업용 도구의 기능성과 효율성을 향상시키고 있습니다. 농가의 업무 효율성이 높아짐에 따라 하드웨어와 소프트웨어가 결합된 종합적인 솔루션에 대한 수요가 증가할 것으로 예측됩니다.

지역별 개요

북미: 북미의 농업용 AI 시장은 첨단 기술 도입과 막대한 연구개발 투자에 힘입어 매우 성숙한 시장으로 성장하고 있습니다. 주요 산업으로는 정밀 농업과 가축 모니터링 등이 있습니다. 미국과 캐나다가 주목해야 할 국가이며, 탄탄한 기술 인프라와 혁신 생태계를 배경으로 미국이 주도적인 위치에 있습니다.

유럽: 유럽 시장은 중간 정도의 성숙도를 보이고 있으며, 높은 성장 잠재력을 가지고 있습니다. 수요는 지속 가능한 농업과 스마트 농업의 노력에 의해 주도되고 있습니다. 독일, 프랑스, 네덜란드가 주목해야 할 국가이며, 농업 기술과 혁신에 중점을 둔 독일이 주도적인 위치에 있습니다.

아시아태평양: 아시아태평양 시장은 빠르게 성장하고 있으며, 식량 수요 증가와 스마트 농업을 지원하는 정부의 이니셔티브에 힘입어 빠르게 성장하고 있습니다. 주요 산업에는 작물 모니터링 및 예측 분석이 포함됩니다. 중국, 인도, 일본이 주목받고 있으며, 대규모 농업 활동과 AI 기술에 대한 투자로 중국이 주도적인 위치에 있습니다.

라틴아메리카: 시장은 신흥 단계에 있으며, 생산성과 지속가능성을 향상시키기 위한 AI 기술 도입이 진행되고 있습니다. 주요 산업에는 작물 관리 및 공급망 최적화가 포함됩니다. 브라질과 아르헨티나가 주목받고 있으며, 광범위한 농업 부문과 기술 도입의 확대로 브라질이 주도적인 위치에 있습니다.

중동 및 아프리카: 시장은 아직 초기 단계에 있지만, 효율적인 자원 관리와 식량 안보의 필요성으로 인해 성장 잠재력을 가지고 있습니다. 주요 산업으로는 관개 관리, 작물 모니터링 등이 있습니다. 남아공과 이스라엘이 주목받고 있으며, 농업 기술 및 물 관리 솔루션의 혁신으로 이스라엘이 주도적인 위치에 있습니다.

주요 동향 및 촉진요인

트렌드1: 정밀농업 기술

정밀농업 기술은 농부들이 농작물 재배와 관련된 밭 단위의 관리를 최적화할 수 있도록 함으로써 농업의 양상을 바꾸고 있습니다. GPS 및 IoT 기반 센서를 포함한 이러한 기술은 데이터에 기반한 의사결정을 촉진하고 물, 비료, 농약 등의 투입물을 정확하게 적용할 수 있게 해줍니다. 이러한 추세는 작물 수확량 증가, 폐기물 감소 및 농장의 전반적인 생산성 향상에 대한 필요성에 의해 주도되고 있으며, 이는 지속 가능한 농업 관행을 향한 전 세계적인 움직임과 일치합니다.

트렌드 2 제목 : AI를 활용한 예측 분석

AI를 활용한 예측 분석은 기상 패턴, 작물 생육 상황, 해충 발생 상황에 대한 인사이트를 제공하며 농업의 기반이 되고 있습니다. 머신러닝 알고리즘을 활용하면 농부들은 잠재적인 문제를 예측하고 수확량에 영향을 미치기 전에 위험을 줄일 수 있습니다. 이러한 추세는 농업 데이터의 가용성 증가와 식량 안보를 보장하고 자원 활용을 최적화하기 위한 미래지향적인 농장 관리 전략에 대한 요구로 인해 더욱 가속화되고 있습니다.

트렌드 3 제목 : 자율형 농기계

드론, 수확용 로봇 등 자율형 농기계 개발 및 도입이 농업 분야에서 활발히 진행되고 있습니다. 이러한 혁신 기술은 인건비 절감과 업무 효율성 향상을 약속합니다. 노동력 부족과 비용 상승이 전통적인 농업 방식에 도전이 되고 있는 가운데, AI와 로봇 기술의 발전에 힘입어 자율형 솔루션의 도입이 가속화될 것으로 예측됩니다.

트렌드 4 제목: 지속가능한 농업 실천

규제 압력과 친환경 제품에 대한 소비자 수요를 배경으로 지속 가능한 농업에 대한 관심이 높아지고 있습니다. AI 기술은 정밀 농업의 실현, 화학물질 사용 감소, 물 관리 개선을 통해 지속가능성을 촉진하는 데 중요한 역할을 하고 있습니다. 이러한 추세는 지속 가능한 농업을 장려하기 위한 정부의 인센티브와 정책에 의해 더욱 촉진되고 있습니다.

트렌드 5 제목 : 공급망 투명성 향상을 위한 블록체인 통합

투명성과 추적성을 향상시키기 위해 농업 공급망에 블록체인 기술이 점점 더 많이 도입되고 있습니다. 블록체인은 안전하고 위변조가 불가능한 거래 기록을 제공함으로써 농산물의 진위 여부와 품질 확보에 기여합니다. 이러한 추세는 투명성에 대한 소비자의 요구에 부응하고 엄격한 식품 안전 규제를 준수해야 할 필요성에 의해 주도되고 있으며, 궁극적으로 농업 공급망의 신뢰와 효율성을 향상시킬 수 있습니다.

목차

제1장 주요 요약

제2장 시장 하이라이트

제3장 시장 역학

제4장 부문 분석

제5장 지역별 분석

제6장 시장 전략

제7장 경쟁 정보

제8장 기업 개요

제9장 당사에 대해

LSH 26.04.16

The global Artificial Intelligence in Agriculture Market is projected to grow from $3.9 billion in 2025 to $9.5 billion by 2035, at a compound annual growth rate (CAGR) of 9.6%. Growth is driven by increasing adoption of precision farming, demand for enhanced crop monitoring, and integration of AI technologies to optimize resource use and yield outcomes. The Artificial Intelligence in Agriculture Market is characterized by a moderately consolidated structure, with the top segments being precision farming (approximately 45% market share), livestock monitoring (30%), and drone analytics (25%). Key applications include crop monitoring, soil management, and predictive analytics, which are driving the adoption of AI technologies in agriculture. The market is witnessing an increase in installations of AI-driven equipment and software solutions, particularly in developed regions where technological infrastructure supports such advancements.

The competitive landscape features a mix of global and regional players, with significant contributions from technology giants and specialized agri-tech firms. The degree of innovation is high, with continuous advancements in machine learning algorithms and data analytics capabilities. There is a notable trend of mergers and acquisitions, as well as strategic partnerships, aimed at expanding technological capabilities and market reach. Companies are increasingly collaborating with research institutions and agricultural cooperatives to enhance product offerings and improve market penetration. This dynamic environment fosters both competition and collaboration, driving the market forward.

Market Segmentation
TypeMachine Learning, Computer Vision, Predictive Analytics, Others
ProductCrop Monitoring, Soil Management, Precision Farming, Livestock Monitoring, Others
ServicesConsulting, System Integration, Support and Maintenance, Others
TechnologyIoT, Big Data, Cloud Computing, Robotics, Others
ComponentHardware, Software, Services, Others
ApplicationYield Monitoring, Field Mapping, Weather Tracking and Forecasting, Crop Scouting, Others
ProcessData Collection, Data Analysis, Decision Making, Others
DeploymentOn-Premises, Cloud-Based, Hybrid, Others
End UserFarmers, Agricultural Corporations, Research Institutions, Others
SolutionsFarm Management Systems, Agricultural Robots, AI-Driven Drones, Others

The Type segment in the AI in Agriculture market is primarily driven by machine learning and computer vision technologies, which are crucial for automating complex tasks such as crop monitoring and predictive analytics. Machine learning dominates due to its ability to process vast amounts of data and generate actionable insights, enhancing decision-making processes. Computer vision is gaining traction with its application in precision farming and autonomous equipment. The increasing adoption of IoT devices in agriculture further fuels growth in this segment.

Technology in AI agriculture is spearheaded by predictive analytics and robotics. Predictive analytics is essential for forecasting crop yields and optimizing resource allocation, thereby improving efficiency and sustainability. Robotics, particularly in harvesting and planting, addresses labor shortages and enhances productivity. The integration of AI with IoT and cloud computing is a notable trend, enabling real-time data processing and remote monitoring, which are critical for modern agricultural practices.

The Application segment is dominated by precision farming and livestock monitoring. Precision farming leverages AI to optimize field-level management regarding crop farming, leading to increased yields and reduced waste. Livestock monitoring uses AI for health monitoring and behavior analysis, improving animal welfare and farm profitability. The growing demand for sustainable farming practices and the need to meet global food security challenges are key drivers in this segment.

End User analysis shows that large-scale commercial farms are the primary adopters of AI technologies due to their capacity to invest in advanced solutions and the scale at which they operate. However, small and medium-sized farms are increasingly adopting AI tools as costs decrease and the benefits of technology become more evident. Government initiatives and subsidies aimed at modernizing agriculture are also encouraging adoption across different farm sizes.

In the Component segment, software solutions lead the market, providing platforms for data management, analytics, and decision support. Hardware components, such as sensors and drones, are also critical, enabling data collection and real-time monitoring. The integration of AI software with existing agricultural equipment is a growing trend, enhancing the functionality and efficiency of traditional farming tools. The demand for comprehensive solutions that combine hardware and software is expected to rise as farmers seek to streamline operations.

Geographical Overview

North America: The AI in agriculture market in North America is highly mature, driven by advanced technological adoption and significant R&D investments. Key industries include precision farming and livestock monitoring. The United States and Canada are notable countries, with the U.S. leading due to its robust tech infrastructure and innovation ecosystem.

Europe: Europe's market is moderately mature, with strong growth potential. The demand is driven by sustainable agriculture and smart farming initiatives. Germany, France, and the Netherlands are notable, with Germany leading due to its focus on agricultural technology and innovation.

Asia-Pacific: The market in Asia-Pacific is rapidly growing, driven by increasing food demand and government initiatives supporting smart agriculture. Key industries include crop monitoring and predictive analytics. China, India, and Japan are notable, with China leading due to its large-scale agricultural activities and investment in AI technologies.

Latin America: The market is emerging, with increasing adoption of AI technologies to enhance productivity and sustainability. Key industries include crop management and supply chain optimization. Brazil and Argentina are notable, with Brazil leading due to its extensive agricultural sector and growing tech adoption.

Middle East & Africa: The market is in its nascent stage, with potential growth driven by the need for efficient resource management and food security. Key industries include irrigation management and crop monitoring. South Africa and Israel are notable, with Israel leading due to its innovation in agricultural technologies and water management solutions.

Key Trends and Drivers

Trend 1 Title: Precision Agriculture Technologies

Precision agriculture technologies are transforming the agricultural landscape by enabling farmers to optimize field-level management regarding crop farming. These technologies, including GPS and IoT-based sensors, facilitate data-driven decision-making, allowing for precise application of inputs like water, fertilizers, and pesticides. This trend is driven by the need to enhance crop yield, reduce waste, and improve overall farm productivity, aligning with the global push towards sustainable farming practices.

Trend 2 Title: AI-Powered Predictive Analytics

AI-powered predictive analytics is becoming a cornerstone in agriculture, offering insights into weather patterns, crop health, and pest infestations. By leveraging machine learning algorithms, farmers can anticipate potential issues and mitigate risks before they impact yields. This trend is fueled by the increasing availability of agricultural data and the need for proactive farm management strategies to ensure food security and optimize resource use.

Trend 3 Title: Autonomous Farming Equipment

The development and deployment of autonomous farming equipment, such as drones and robotic harvesters, are gaining traction in the agriculture sector. These innovations promise to reduce labor costs and increase operational efficiency. As labor shortages and rising costs challenge traditional farming methods, the adoption of autonomous solutions is expected to accelerate, supported by advancements in AI and robotics technologies.

Trend 4 Title: Sustainable Farming Practices

There is a growing emphasis on sustainable farming practices, driven by regulatory pressures and consumer demand for environmentally friendly products. AI technologies are playing a crucial role in promoting sustainability by enabling precision farming, reducing chemical usage, and improving water management. This trend is further supported by government incentives and policies aimed at encouraging sustainable agricultural practices.

Trend 5 Title: Integration of Blockchain for Supply Chain Transparency

Blockchain technology is increasingly being integrated into agricultural supply chains to enhance transparency and traceability. By providing a secure and immutable record of transactions, blockchain helps ensure the authenticity and quality of agricultural products. This trend is driven by the need to meet consumer demands for transparency and to comply with stringent food safety regulations, ultimately fostering trust and efficiency in the agricultural supply chain.

Research Scope

  • Estimates and forecasts the overall market size across type, application, and region.
  • Provides detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling.
  • Identifies factors influencing market growth and challenges, opportunities, drivers, and restraints.
  • Identifies factors that could limit company participation in international markets to help calibrate market share expectations and growth rates.
  • Evaluates key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities.
  • Analyzes smaller market segments strategically, focusing on their potential, growth patterns, and impact on the overall market.
  • Outlines the competitive landscape, assessing business and corporate strategies to monitor and dissect competitive advancements.

Our research scope provides comprehensive market data, insights, and analysis across a variety of critical areas. We cover Local Market Analysis, assessing consumer demographics, purchasing behaviors, and market size within specific regions to identify growth opportunities. Our Local Competition Review offers a detailed evaluation of competitors, including their strengths, weaknesses, and market positioning. We also conduct Local Regulatory Reviews to ensure businesses comply with relevant laws and regulations. Industry Analysis provides an in-depth look at market dynamics, key players, and trends. Additionally, we offer Cross-Segmental Analysis to identify synergies between different market segments, as well as Production-Consumption and Demand-Supply Analysis to optimize supply chain efficiency. Our Import-Export Analysis helps businesses navigate global trade environments by evaluating trade flows and policies. These insights empower clients to make informed strategic decisions, mitigate risks, and capitalize on market opportunities.

TABLE OF CONTENTS

1 Executive Summary

  • 1.1 Market Size and Forecast
  • 1.2 Market Overview
  • 1.3 Market Snapshot
  • 1.4 Regional Snapshot
  • 1.5 Strategic Recommendations
  • 1.6 Analyst Notes

2 Market Highlights

  • 2.1 Key Market Highlights by Type
  • 2.2 Key Market Highlights by Product
  • 2.3 Key Market Highlights by Services
  • 2.4 Key Market Highlights by Technology
  • 2.5 Key Market Highlights by Component
  • 2.6 Key Market Highlights by Application
  • 2.7 Key Market Highlights by Process
  • 2.8 Key Market Highlights by Deployment
  • 2.9 Key Market Highlights by End User
  • 2.10 Key Market Highlights by Solutions

3 Market Dynamics

  • 3.1 Macroeconomic Analysis
  • 3.2 Market Trends
  • 3.3 Market Drivers
  • 3.4 Market Opportunities
  • 3.5 Market Restraints
  • 3.6 CAGR Growth Analysis
  • 3.7 Impact Analysis
  • 3.8 Emerging Markets
  • 3.9 Technology Roadmap
  • 3.10 Strategic Frameworks
    • 3.10.1 PORTER's 5 Forces Model
    • 3.10.2 ANSOFF Matrix
    • 3.10.3 4P's Model
    • 3.10.4 PESTEL Analysis

4 Segment Analysis

  • 4.1 Market Size & Forecast by Type (2020-2035)
    • 4.1.1 Machine Learning
    • 4.1.2 Computer Vision
    • 4.1.3 Predictive Analytics
    • 4.1.4 Others
  • 4.2 Market Size & Forecast by Product (2020-2035)
    • 4.2.1 Crop Monitoring
    • 4.2.2 Soil Management
    • 4.2.3 Precision Farming
    • 4.2.4 Livestock Monitoring
    • 4.2.5 Others
  • 4.3 Market Size & Forecast by Services (2020-2035)
    • 4.3.1 Consulting
    • 4.3.2 System Integration
    • 4.3.3 Support and Maintenance
    • 4.3.4 Others
  • 4.4 Market Size & Forecast by Technology (2020-2035)
    • 4.4.1 IoT
    • 4.4.2 Big Data
    • 4.4.3 Cloud Computing
    • 4.4.4 Robotics
    • 4.4.5 Others
  • 4.5 Market Size & Forecast by Component (2020-2035)
    • 4.5.1 Hardware
    • 4.5.2 Software
    • 4.5.3 Services
    • 4.5.4 Others
  • 4.6 Market Size & Forecast by Application (2020-2035)
    • 4.6.1 Yield Monitoring
    • 4.6.2 Field Mapping
    • 4.6.3 Weather Tracking and Forecasting
    • 4.6.4 Crop Scouting
    • 4.6.5 Others
  • 4.7 Market Size & Forecast by Process (2020-2035)
    • 4.7.1 Data Collection
    • 4.7.2 Data Analysis
    • 4.7.3 Decision Making
    • 4.7.4 Others
  • 4.8 Market Size & Forecast by Deployment (2020-2035)
    • 4.8.1 On-Premises
    • 4.8.2 Cloud-Based
    • 4.8.3 Hybrid
    • 4.8.4 Others
  • 4.9 Market Size & Forecast by End User (2020-2035)
    • 4.9.1 Farmers
    • 4.9.2 Agricultural Corporations
    • 4.9.3 Research Institutions
    • 4.9.4 Others
  • 4.10 Market Size & Forecast by Solutions (2020-2035)
    • 4.10.1 Farm Management Systems
    • 4.10.2 Agricultural Robots
    • 4.10.3 AI-Driven Drones
    • 4.10.4 Others

5 Regional Analysis

  • 5.1 Global Market Overview
  • 5.2 North America Market Size (2020-2035)
    • 5.2.1 United States
      • 5.2.1.1 Type
      • 5.2.1.2 Product
      • 5.2.1.3 Services
      • 5.2.1.4 Technology
      • 5.2.1.5 Component
      • 5.2.1.6 Application
      • 5.2.1.7 Process
      • 5.2.1.8 Deployment
      • 5.2.1.9 End User
      • 5.2.1.10 Solutions
    • 5.2.2 Canada
      • 5.2.2.1 Type
      • 5.2.2.2 Product
      • 5.2.2.3 Services
      • 5.2.2.4 Technology
      • 5.2.2.5 Component
      • 5.2.2.6 Application
      • 5.2.2.7 Process
      • 5.2.2.8 Deployment
      • 5.2.2.9 End User
      • 5.2.2.10 Solutions
    • 5.2.3 Mexico
      • 5.2.3.1 Type
      • 5.2.3.2 Product
      • 5.2.3.3 Services
      • 5.2.3.4 Technology
      • 5.2.3.5 Component
      • 5.2.3.6 Application
      • 5.2.3.7 Process
      • 5.2.3.8 Deployment
      • 5.2.3.9 End User
      • 5.2.3.10 Solutions
  • 5.3 Latin America Market Size (2020-2035)
    • 5.3.1 Brazil
      • 5.3.1.1 Type
      • 5.3.1.2 Product
      • 5.3.1.3 Services
      • 5.3.1.4 Technology
      • 5.3.1.5 Component
      • 5.3.1.6 Application
      • 5.3.1.7 Process
      • 5.3.1.8 Deployment
      • 5.3.1.9 End User
      • 5.3.1.10 Solutions
    • 5.3.2 Argentina
      • 5.3.2.1 Type
      • 5.3.2.2 Product
      • 5.3.2.3 Services
      • 5.3.2.4 Technology
      • 5.3.2.5 Component
      • 5.3.2.6 Application
      • 5.3.2.7 Process
      • 5.3.2.8 Deployment
      • 5.3.2.9 End User
      • 5.3.2.10 Solutions
    • 5.3.3 Rest of Latin America
      • 5.3.3.1 Type
      • 5.3.3.2 Product
      • 5.3.3.3 Services
      • 5.3.3.4 Technology
      • 5.3.3.5 Component
      • 5.3.3.6 Application
      • 5.3.3.7 Process
      • 5.3.3.8 Deployment
      • 5.3.3.9 End User
      • 5.3.3.10 Solutions
  • 5.4 Asia-Pacific Market Size (2020-2035)
    • 5.4.1 China
      • 5.4.1.1 Type
      • 5.4.1.2 Product
      • 5.4.1.3 Services
      • 5.4.1.4 Technology
      • 5.4.1.5 Component
      • 5.4.1.6 Application
      • 5.4.1.7 Process
      • 5.4.1.8 Deployment
      • 5.4.1.9 End User
      • 5.4.1.10 Solutions
    • 5.4.2 India
      • 5.4.2.1 Type
      • 5.4.2.2 Product
      • 5.4.2.3 Services
      • 5.4.2.4 Technology
      • 5.4.2.5 Component
      • 5.4.2.6 Application
      • 5.4.2.7 Process
      • 5.4.2.8 Deployment
      • 5.4.2.9 End User
      • 5.4.2.10 Solutions
    • 5.4.3 South Korea
      • 5.4.3.1 Type
      • 5.4.3.2 Product
      • 5.4.3.3 Services
      • 5.4.3.4 Technology
      • 5.4.3.5 Component
      • 5.4.3.6 Application
      • 5.4.3.7 Process
      • 5.4.3.8 Deployment
      • 5.4.3.9 End User
      • 5.4.3.10 Solutions
    • 5.4.4 Japan
      • 5.4.4.1 Type
      • 5.4.4.2 Product
      • 5.4.4.3 Services
      • 5.4.4.4 Technology
      • 5.4.4.5 Component
      • 5.4.4.6 Application
      • 5.4.4.7 Process
      • 5.4.4.8 Deployment
      • 5.4.4.9 End User
      • 5.4.4.10 Solutions
    • 5.4.5 Australia
      • 5.4.5.1 Type
      • 5.4.5.2 Product
      • 5.4.5.3 Services
      • 5.4.5.4 Technology
      • 5.4.5.5 Component
      • 5.4.5.6 Application
      • 5.4.5.7 Process
      • 5.4.5.8 Deployment
      • 5.4.5.9 End User
      • 5.4.5.10 Solutions
    • 5.4.6 Taiwan
      • 5.4.6.1 Type
      • 5.4.6.2 Product
      • 5.4.6.3 Services
      • 5.4.6.4 Technology
      • 5.4.6.5 Component
      • 5.4.6.6 Application
      • 5.4.6.7 Process
      • 5.4.6.8 Deployment
      • 5.4.6.9 End User
      • 5.4.6.10 Solutions
    • 5.4.7 Rest of APAC
      • 5.4.7.1 Type
      • 5.4.7.2 Product
      • 5.4.7.3 Services
      • 5.4.7.4 Technology
      • 5.4.7.5 Component
      • 5.4.7.6 Application
      • 5.4.7.7 Process
      • 5.4.7.8 Deployment
      • 5.4.7.9 End User
      • 5.4.7.10 Solutions
  • 5.5 Europe Market Size (2020-2035)
    • 5.5.1 Germany
      • 5.5.1.1 Type
      • 5.5.1.2 Product
      • 5.5.1.3 Services
      • 5.5.1.4 Technology
      • 5.5.1.5 Component
      • 5.5.1.6 Application
      • 5.5.1.7 Process
      • 5.5.1.8 Deployment
      • 5.5.1.9 End User
      • 5.5.1.10 Solutions
    • 5.5.2 France
      • 5.5.2.1 Type
      • 5.5.2.2 Product
      • 5.5.2.3 Services
      • 5.5.2.4 Technology
      • 5.5.2.5 Component
      • 5.5.2.6 Application
      • 5.5.2.7 Process
      • 5.5.2.8 Deployment
      • 5.5.2.9 End User
      • 5.5.2.10 Solutions
    • 5.5.3 United Kingdom
      • 5.5.3.1 Type
      • 5.5.3.2 Product
      • 5.5.3.3 Services
      • 5.5.3.4 Technology
      • 5.5.3.5 Component
      • 5.5.3.6 Application
      • 5.5.3.7 Process
      • 5.5.3.8 Deployment
      • 5.5.3.9 End User
      • 5.5.3.10 Solutions
    • 5.5.4 Spain
      • 5.5.4.1 Type
      • 5.5.4.2 Product
      • 5.5.4.3 Services
      • 5.5.4.4 Technology
      • 5.5.4.5 Component
      • 5.5.4.6 Application
      • 5.5.4.7 Process
      • 5.5.4.8 Deployment
      • 5.5.4.9 End User
      • 5.5.4.10 Solutions
    • 5.5.5 Italy
      • 5.5.5.1 Type
      • 5.5.5.2 Product
      • 5.5.5.3 Services
      • 5.5.5.4 Technology
      • 5.5.5.5 Component
      • 5.5.5.6 Application
      • 5.5.5.7 Process
      • 5.5.5.8 Deployment
      • 5.5.5.9 End User
      • 5.5.5.10 Solutions
    • 5.5.6 Rest of Europe
      • 5.5.6.1 Type
      • 5.5.6.2 Product
      • 5.5.6.3 Services
      • 5.5.6.4 Technology
      • 5.5.6.5 Component
      • 5.5.6.6 Application
      • 5.5.6.7 Process
      • 5.5.6.8 Deployment
      • 5.5.6.9 End User
      • 5.5.6.10 Solutions
  • 5.6 Middle East & Africa Market Size (2020-2035)
    • 5.6.1 Saudi Arabia
      • 5.6.1.1 Type
      • 5.6.1.2 Product
      • 5.6.1.3 Services
      • 5.6.1.4 Technology
      • 5.6.1.5 Component
      • 5.6.1.6 Application
      • 5.6.1.7 Process
      • 5.6.1.8 Deployment
      • 5.6.1.9 End User
      • 5.6.1.10 Solutions
    • 5.6.2 United Arab Emirates
      • 5.6.2.1 Type
      • 5.6.2.2 Product
      • 5.6.2.3 Services
      • 5.6.2.4 Technology
      • 5.6.2.5 Component
      • 5.6.2.6 Application
      • 5.6.2.7 Process
      • 5.6.2.8 Deployment
      • 5.6.2.9 End User
      • 5.6.2.10 Solutions
    • 5.6.3 South Africa
      • 5.6.3.1 Type
      • 5.6.3.2 Product
      • 5.6.3.3 Services
      • 5.6.3.4 Technology
      • 5.6.3.5 Component
      • 5.6.3.6 Application
      • 5.6.3.7 Process
      • 5.6.3.8 Deployment
      • 5.6.3.9 End User
      • 5.6.3.10 Solutions
    • 5.6.4 Sub-Saharan Africa
      • 5.6.4.1 Type
      • 5.6.4.2 Product
      • 5.6.4.3 Services
      • 5.6.4.4 Technology
      • 5.6.4.5 Component
      • 5.6.4.6 Application
      • 5.6.4.7 Process
      • 5.6.4.8 Deployment
      • 5.6.4.9 End User
      • 5.6.4.10 Solutions
    • 5.6.5 Rest of MEA
      • 5.6.5.1 Type
      • 5.6.5.2 Product
      • 5.6.5.3 Services
      • 5.6.5.4 Technology
      • 5.6.5.5 Component
      • 5.6.5.6 Application
      • 5.6.5.7 Process
      • 5.6.5.8 Deployment
      • 5.6.5.9 End User
      • 5.6.5.10 Solutions

6 Market Strategy

  • 6.1 Demand-Supply Gap Analysis
  • 6.2 Trade & Logistics Constraints
  • 6.3 Price-Cost-Margin Trends
  • 6.4 Market Penetration
  • 6.5 Consumer Analysis
  • 6.6 Regulatory Snapshot

7 Competitive Intelligence

  • 7.1 Market Positioning
  • 7.2 Market Share
  • 7.3 Competition Benchmarking
  • 7.4 Top Company Strategies

8 Company Profiles

  • 8.1 John Deere
    • 8.1.1 Overview
    • 8.1.2 Product Summary
    • 8.1.3 Financial Performance
    • 8.1.4 SWOT Analysis
  • 8.2 Trimble
    • 8.2.1 Overview
    • 8.2.2 Product Summary
    • 8.2.3 Financial Performance
    • 8.2.4 SWOT Analysis
  • 8.3 AGCO Corporation
    • 8.3.1 Overview
    • 8.3.2 Product Summary
    • 8.3.3 Financial Performance
    • 8.3.4 SWOT Analysis
  • 8.4 Deere and Company
    • 8.4.1 Overview
    • 8.4.2 Product Summary
    • 8.4.3 Financial Performance
    • 8.4.4 SWOT Analysis
  • 8.5 BASF
    • 8.5.1 Overview
    • 8.5.2 Product Summary
    • 8.5.3 Financial Performance
    • 8.5.4 SWOT Analysis
  • 8.6 Bayer
    • 8.6.1 Overview
    • 8.6.2 Product Summary
    • 8.6.3 Financial Performance
    • 8.6.4 SWOT Analysis
  • 8.7 CNH Industrial
    • 8.7.1 Overview
    • 8.7.2 Product Summary
    • 8.7.3 Financial Performance
    • 8.7.4 SWOT Analysis
  • 8.8 Syngenta
    • 8.8.1 Overview
    • 8.8.2 Product Summary
    • 8.8.3 Financial Performance
    • 8.8.4 SWOT Analysis
  • 8.9 Yara International
    • 8.9.1 Overview
    • 8.9.2 Product Summary
    • 8.9.3 Financial Performance
    • 8.9.4 SWOT Analysis
  • 8.10 Corteva Agriscience
    • 8.10.1 Overview
    • 8.10.2 Product Summary
    • 8.10.3 Financial Performance
    • 8.10.4 SWOT Analysis
  • 8.11 Kubota Corporation
    • 8.11.1 Overview
    • 8.11.2 Product Summary
    • 8.11.3 Financial Performance
    • 8.11.4 SWOT Analysis
  • 8.12 Raven Industries
    • 8.12.1 Overview
    • 8.12.2 Product Summary
    • 8.12.3 Financial Performance
    • 8.12.4 SWOT Analysis
  • 8.13 Topcon Positioning Systems
    • 8.13.1 Overview
    • 8.13.2 Product Summary
    • 8.13.3 Financial Performance
    • 8.13.4 SWOT Analysis
  • 8.14 Hexagon Agriculture
    • 8.14.1 Overview
    • 8.14.2 Product Summary
    • 8.14.3 Financial Performance
    • 8.14.4 SWOT Analysis
  • 8.15 AG Leader Technology
    • 8.15.1 Overview
    • 8.15.2 Product Summary
    • 8.15.3 Financial Performance
    • 8.15.4 SWOT Analysis
  • 8.16 The Climate Corporation
    • 8.16.1 Overview
    • 8.16.2 Product Summary
    • 8.16.3 Financial Performance
    • 8.16.4 SWOT Analysis
  • 8.17 Farmers Edge
    • 8.17.1 Overview
    • 8.17.2 Product Summary
    • 8.17.3 Financial Performance
    • 8.17.4 SWOT Analysis
  • 8.18 Taranis
    • 8.18.1 Overview
    • 8.18.2 Product Summary
    • 8.18.3 Financial Performance
    • 8.18.4 SWOT Analysis
  • 8.19 Prospera Technologies
    • 8.19.1 Overview
    • 8.19.2 Product Summary
    • 8.19.3 Financial Performance
    • 8.19.4 SWOT Analysis
  • 8.20 Gamaya
    • 8.20.1 Overview
    • 8.20.2 Product Summary
    • 8.20.3 Financial Performance
    • 8.20.4 SWOT Analysis

9 About Us

  • 9.1 About Us
  • 9.2 Research Methodology
  • 9.3 Research Workflow
  • 9.4 Consulting Services
  • 9.5 Our Clients
  • 9.6 Client Testimonials
  • 9.7 Contact Us
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