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Artificial Intelligence in Agriculture Market - Global Forecast 2025-2032

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    Report

  • 183 Pages
  • October 2025
  • Region: Global
  • 360iResearch™
  • ID: 5612799
UP TO OFF until Jan 01st 2026
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Artificial intelligence in agriculture is reshaping how the industry approaches efficiency, sustainability, and risk mitigation. Senior leaders are increasingly investing in next-generation technology to transform core processes and remain competitive in a changing regulatory and economic environment.

Market Snapshot: Artificial Intelligence in Agriculture Market

The Artificial Intelligence in Agriculture Market is experiencing robust expansion, moving from USD 2.73 billion in 2024 to USD 3.38 billion in 2025. With a projected CAGR of 23.25%, the market is forecast to achieve USD 14.55 billion by 2032, driven by growing digitization, advancements in machine learning, and the adoption of data-driven approaches across farming and agribusiness.

Scope & Segmentation

This report provides comprehensive analysis and market forecasts across the following segments, ensuring senior stakeholders can identify high-impact growth opportunities and benchmark against competitive offerings:

  • Offering: Hardware (Cameras, Drone Hardware, Sensors), Services (Consulting, Implementation, Support), Software (Analytics Platforms, Decision Support Systems, Farm Management Systems)
  • Application: Crop Monitoring (Disease Detection, Irrigation Management, Nutrient Management), Livestock Management (Feed Optimization, Health Monitoring), Soil Health (Soil Analysis, Soil Moisture Monitoring), Supply Chain Management (Logistics Optimization, Traceability), Yield Prediction (Crop Modeling, Weather Forecasting)
  • Technology: Drones (Fixed Wing, Rotary Wing), Machine Vision (Optical Sensors, Thermal Cameras), Predictive Analytics (AI Algorithms, Machine Learning Models), Robotics (Automated Harvesters, Autonomous Tractors)
  • Deployment Mode: Cloud, Hybrid, On Premise
  • End User: Agrochemical Companies, Large Farms, Research Institutes, Small & Medium Farms
  • Regions: Americas (United States, Canada, Mexico, Brazil, Argentina, Chile, Colombia, Peru), Europe, Middle East & Africa (United Kingdom, Germany, France, Russia, Italy, Spain, Netherlands, Sweden, Poland, Switzerland, United Arab Emirates, Saudi Arabia, Qatar, Turkey, Israel, South Africa, Nigeria, Egypt, Kenya), Asia-Pacific (China, India, Japan, Australia, South Korea, Indonesia, Thailand, Malaysia, Singapore, Taiwan)
  • Leading Companies: Deere & Company, Bayer AG, International Business Machines Corporation, Trimble Inc., CNH Industrial N.V., BASF SE, Syngenta AG, Farmers Edge Limited, Yara International ASA, Taranis Inc.

Key Takeaways for Senior Decision-Makers

  • AI-driven solutions are enabling predictive analytics, automation, and real-time monitoring from field to supply chain, offering actionable insights for operational leaders.
  • Collaborative ecosystems are rapidly forming, connecting technology vendors, agritech start-ups, and research institutions to accelerate new product deployment and regional innovation.
  • Precision agriculture tools, including drones and robotics, are now accessible to a broad range of producers, not just large commercial farms, thanks to lower sensor costs and cloud models.
  • Software providers are focusing on seamless integration and intuitive interfaces to boost user adoption across diverse farm sizes and operational needs.
  • Regional adoption patterns are influenced by infrastructure, local regulations, and sustainability agendas, requiring adaptive deployment strategies and compliance-ready technologies.
  • Strategic alliances and modular platform development are emerging as critical differentiators for companies seeking to provide scalable, interoperable solutions.

Tariff Impact & Adaptive Strategies

Recent introduction of United States Tariffs 2025 has influenced global supply chain structures in agricultural technology, resulting in increased capital expenditures for imported components such as sensors and drone parts. In response, organizations are pivoting to regional manufacturing and partnership models to minimize exposure and drive faster adaptation. These shifts are fostering greater self-sufficiency and resilience within domestic markets, while also catalyzing the establishment of specialized innovation hubs customized to local crop demands. Global technology providers are also reconfiguring their value chains, establishing decentralized facilities and broadening support operations to ensure regulatory compliance and fluid service delivery.

Methodology & Data Sources

This research combines in-depth primary interviews with senior executives, agronomists, and technology leaders, field observations, and comprehensive analysis of scientific literature, patents, and technical white papers. Regional case studies and competitive benchmarking provide real-world insight. Data reliability is ensured through triangulation of qualitative trends and quantitative indicators.

Why This Report Matters

  • Enables executives to drive evidence-based investment, partnership, and operational strategies in artificial intelligence for agriculture.
  • Delivers actionable segmentation and market benchmarks critical for competitive positioning, product innovation, and regulatory compliance planning.
  • Empowers strategic planning by revealing region-specific drivers, technology adoption models, and supply chain adaptations in response to new policy developments.

Conclusion

Artificial intelligence in agriculture is unlocking new levels of productivity and sustainability. Leaders equipped with deep, actionable market intelligence will secure long-term resilience and value as digital transformation reshapes the global industry.

 

Additional Product Information:

  • Purchase of this report includes 1 year online access with quarterly updates.
  • This report can be updated on request. Please contact our Customer Experience team using the Ask a Question widget on our website.

Table of Contents

1. Preface
1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders
2. Research Methodology
3. Executive Summary
4. Market Overview
5. Market Insights
5.1. AI-driven precision irrigation systems optimizing water usage based on soil moisture and weather forecasts
5.2. Implementation of machine learning algorithms in drones for early detection of crop stress and pest infestations
5.3. Adoption of computer vision-enabled robotics for automated harvesting and selective crop picking
5.4. Integration of AI-based predictive analytics with weather data to forecast optimal planting windows
5.5. Use of deep learning models combined with genomic and phenotypic data for accelerated crop breeding
5.6. Deployment of blockchain-enabled AI platforms for transparent agricultural supply chain management and traceability
6. Cumulative Impact of United States Tariffs 2025
7. Cumulative Impact of Artificial Intelligence 2025
8. Artificial Intelligence in Agriculture Market, by Offering
8.1. Hardware
8.1.1. Cameras
8.1.2. Drone Hardware
8.1.3. Sensors
8.2. Services
8.2.1. Consulting
8.2.2. Implementation
8.2.3. Support
8.3. Software
8.3.1. Analytics Platforms
8.3.2. Decision Support Systems
8.3.3. Farm Management Systems
9. Artificial Intelligence in Agriculture Market, by Application
9.1. Crop Monitoring
9.1.1. Disease Detection
9.1.2. Irrigation Management
9.1.3. Nutrient Management
9.2. Livestock Management
9.2.1. Feed Optimization
9.2.2. Health Monitoring
9.3. Soil Health
9.3.1. Soil Analysis
9.3.2. Soil Moisture Monitoring
9.4. Supply Chain Management
9.4.1. Logistics Optimization
9.4.2. Traceability
9.5. Yield Prediction
9.5.1. Crop Modeling
9.5.2. Weather Forecasting
10. Artificial Intelligence in Agriculture Market, by Technology
10.1. Drones
10.1.1. Fixed Wing
10.1.2. Rotary Wing
10.2. Machine Vision
10.2.1. Optical Sensors
10.2.2. Thermal Cameras
10.3. Predictive Analytics
10.3.1. AI Algorithms
10.3.2. Machine Learning Models
10.4. Robotics
10.4.1. Automated Harvesters
10.4.2. Autonomous Tractors
11. Artificial Intelligence in Agriculture Market, by Deployment Mode
11.1. Cloud
11.2. Hybrid
11.3. On Premise
12. Artificial Intelligence in Agriculture Market, by End User
12.1. Agrochemical Companies
12.2. Large Farms
12.3. Research Institutes
12.4. Small & Medium Farms
13. Artificial Intelligence in Agriculture Market, by Region
13.1. Americas
13.1.1. North America
13.1.2. Latin America
13.2. Europe, Middle East & Africa
13.2.1. Europe
13.2.2. Middle East
13.2.3. Africa
13.3. Asia-Pacific
14. Artificial Intelligence in Agriculture Market, by Group
14.1. ASEAN
14.2. GCC
14.3. European Union
14.4. BRICS
14.5. G7
14.6. NATO
15. Artificial Intelligence in 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 Share Analysis, 2024
16.2. FPNV Positioning Matrix, 2024
16.3. Competitive Analysis
16.3.1. Deere & Company
16.3.2. Bayer AG
16.3.3. International Business Machines Corporation
16.3.4. Trimble Inc.
16.3.5. CNH Industrial N.V.
16.3.6. BASF SE
16.3.7. Syngenta AG
16.3.8. Farmers Edge Limited
16.3.9. Yara International ASA
16.3.10. Taranis Inc.
List of Tables
List of Figures

Samples

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Companies Mentioned

The key companies profiled in this Artificial Intelligence in Agriculture market report include:
  • Deere & Company
  • Bayer AG
  • International Business Machines Corporation
  • Trimble Inc.
  • CNH Industrial N.V.
  • BASF SE
  • Syngenta AG
  • Farmers Edge Limited
  • Yara International ASA
  • Taranis Inc.

Table Information