United States AI in Agriculture Market to Reach US$ 4.77 Billion by 2032 | CAGR 25.60%| Asia Pacific leads with 32% share |Key players - Microsoft, IBM, Deere & Company, Bayer AG, Google LLC, Cropin

Leander, Texas and Tokyo, Japan - Feb.2.2026
Global AI in Agriculture Market reached US$ 2,567.54 million in 2024 and is expected to reach US$ 15,901.36 million by 2032, growing with a CAGR of 25.60% during the forecast period 2025-2032, according to DataM Intelligence report.

The market is driven by the need to improve crop productivity and address labor shortages. AI-powered tools such as precision farming software, crop monitoring systems, and predictive analytics dominate adoption. Applications span crop management, livestock monitoring, and supply chain optimization. Government initiatives for digital agriculture and rising demand for sustainable farming practices fuel market growth.

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AI in Agriculture Market: Competitive Intelligence
Microsoft, IBM, Granular Inc., AgEagle Aerial Systems Inc., Google LLC, Deere & Company, Cropin Technology Solutions Private Limited, Bayer AG, KissanAI, and Niqo Robotics

The AI in Agriculture Market is rapidly transforming global farming systems through the combined influence of Microsoft, IBM, Granular Inc., AgEagle Aerial Systems Inc., Google LLC, Deere & Company, Cropin Technology Solutions Private Limited, Bayer AG, KissanAI, and Niqo Robotics. Together, these companies are enabling data-driven agriculture across precision farming, crop monitoring, yield forecasting, pest and disease detection, supply-chain optimization, and autonomous field operations. By integrating artificial intelligence with cloud computing, IoT sensors, satellite imagery, drones, and robotics, they are helping farmers improve productivity, reduce input costs, and enhance climate resilience across both large-scale commercial farms and smallholder agricultural ecosystems.

The collective strength of these players deepens the competitive foundation of the AI in Agriculture Market by blending global technology leadership with domain-specific agricultural expertise. Microsoft, IBM, and Google LLC provide scalable cloud platforms, advanced analytics, and machine learning infrastructure that power real-time decision-making at farm and enterprise levels. Deere & Company, Bayer AG, Granular Inc., and AgEagle Aerial Systems Inc. bridge digital intelligence with on-field execution through smart machinery, digital agronomy tools, aerial imaging, and crop management platforms. Meanwhile, Cropin Technology Solutions Private Limited, KissanAI, and Niqo Robotics bring localized innovation, focusing on farmer advisory systems, vernacular AI solutions, and autonomous robotics tailored to emerging markets. This balanced mix of scale, specialization, and regional adaptability positions the AI in Agriculture Market for sustained growth as global food systems increasingly depend on intelligent, efficient, and sustainable farming practices.

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Fund Raisings
✅ January 2026 - CropX Technologies (Israel)
CropX secured growth funding to scale its AI-driven soil intelligence and farm analytics platform. The investment supports advanced machine-learning models that optimize irrigation, nutrient management, and yield prediction for precision agriculture.

✅ December 2025 - Taranis (United States)
Taranis raised strategic capital to expand its AI-powered crop monitoring solutions using aerial imagery and computer vision. The funding strengthens early pest, disease, and nutrient deficiency detection across large-scale farms.

✅ November 2025 - Agrosmart (Brazil)
Agrosmart attracted fresh investment to enhance its AI-based climate and decision-support tools for farmers. The funding focuses on improving predictive analytics for weather risk management, crop planning, and sustainable resource use.

Product Launches & Strategic Partnerships
✅ January 2026 - John Deere (United States)
John Deere launched an AI-powered precision agriculture platform that uses computer vision and machine learning to optimize planting, spraying, and harvesting. The solution helps farmers reduce input costs while improving crop yield and sustainability.

✅ December 2025 - Microsoft & Bayer (United States / Germany)
Microsoft partnered with Bayer to integrate AI-driven analytics into digital farming tools. The collaboration enables real-time crop monitoring, disease prediction, and data-backed decision-making for large-scale and smallholder farmers.

✅ October 2025 - Trimble Inc. (United States)
Trimble introduced an AI-based farm management system combining satellite imagery and predictive analytics. The platform supports soil health assessment, irrigation optimization, and yield forecasting to improve farm productivity.

Mergers & Acquisitions
✅ January 2026 - John Deere (United States)
John Deere acquired an AI-powered precision farming startup to enhance autonomous equipment and data-driven crop management. The acquisition strengthens Deere's capabilities in smart machinery, yield optimization, and real-time field analytics.

✅ December 2025 - Bayer AG (Germany)
Bayer expanded its digital farming division through the acquisition of an AI-based agronomy analytics company. The move accelerates the integration of machine learning into crop protection, soil health monitoring, and decision-support platforms for farmers.

✅ October 2025 - Trimble Inc. (United States)
Trimble completed the acquisition of an AI-enabled agricultural software firm specializing in predictive analytics and farm automation. This strategic deal enhances Trimble's precision agriculture portfolio and supports sustainable, data-driven farming practices.

Segments Covered in the AI in Agriculture Market:
By Deployment
The market is segmented into Cloud-Based 55%, On-Premise 30%, and Hybrid 15%, with cloud-based deployment dominating due to scalability, real-time data access, and lower infrastructure costs. On-premise solutions remain relevant for large agribusinesses requiring data control, while hybrid models are gaining traction for flexible integration.

By Technology
Technologies include Machine Learning & Deep Learning 40%, Computer Vision 25%, Predictive Analytics 20%, Robotics & Automation 10%, and Others 5%. Machine learning leads by enabling yield prediction, disease detection, and decision support, while computer vision is rapidly expanding in crop monitoring and quality inspection.

By Offering
Offerings consist of Hardware 35%, Software Platforms 45%, and Services 20%. Software platforms dominate due to AI-driven analytics, farm management systems, and decision-support tools, while hardware such as drones, sensors, and autonomous machinery remains essential for data collection.

By Application
Applications include Precision Farming 30%, Crop & Soil Monitoring 25%, Livestock Monitoring 15%, Smart Irrigation 10%, Weather Forecasting 10%, and Others 10%. Precision farming leads by optimizing inputs and maximizing yield, while crop monitoring and irrigation are expanding due to water scarcity and climate variability.

By Farm Size
The market is segmented into Large Farms 45%, Medium Farms 35%, and Small Farms 20%. Large farms dominate adoption due to higher investment capacity, while medium and small farms are increasingly adopting AI solutions through affordable SaaS models and government-supported digital agriculture initiatives.

By End-User
End-users include Commercial Farms 40%, Agribusiness Companies 25%, Agricultural Cooperatives 15%, Research Institutions 10%, and Others 10%. Commercial farms lead adoption to improve productivity and profitability, while agribusiness companies leverage AI for supply chain optimization and demand forecasting.

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Regional Analysis
North America - 28% Share
North America holds around 28% share, driven by early adoption of precision farming, advanced data analytics, and strong investments in agri-tech startups. The U.S. and Canada lead in AI-powered farm management, autonomous machinery, and predictive analytics for crop yield and soil health.

Europe - 25% Share
Europe accounts for nearly 25% share, supported by sustainable farming initiatives, smart greenhouse technologies, and strict environmental regulations. Countries like Germany, France, the Netherlands, and the UK are actively integrating AI for crop monitoring, livestock management, and climate-smart agriculture.

Asia Pacific - 32% Share
Asia Pacific dominates with about 32% share due to large agricultural economies, growing food demand, and rapid digitalization. China, India, Japan, and Southeast Asia are leveraging AI for precision irrigation, pest and disease detection, yield forecasting, and small-farmer advisory platforms.

Latin America - 8% Share
Latin America represents approximately 8% share, fueled by expanding commercial farming and export-oriented agriculture. Brazil, Argentina, and Mexico are adopting AI for crop optimization, satellite-based monitoring, and farm automation to improve productivity and reduce costs.

Middle East & Africa - 7% Share
Middle East & Africa holds around 7% share, with AI adoption focused on water-efficient farming, desert agriculture, and smart irrigation systems. GCC countries and South Africa are investing in AI to enhance food security, optimize resource use, and support climate-resilient agriculture.

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✅ Competitive Landscape
✅ Technology Roadmap Analysis
✅ Sustainability Impact Analysis
✅ KOL / Stakeholder Insights
✅ Consumer Behavior & Demand Analysis
✅ Import-Export Data Monitoring
✅ Live Market & Pricing Trends

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