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Add to CartGlobal Artificial Intelligence in Agriculture Market Growth, Share, Size, Trends and Forecast (2025 - 2031)
By Technology;
Machine Learning, Predictive Analytics, and Computer VisionBy Offering;
Hardware, Software, and ServicesBy Application;
Precision Farming and Drone AnalyticsBy End User;
Farms, Agro-Tech Companies, Agrochemical Companies, Research Institutes, and OthersBy Geography;
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America - Report Timeline (2021 - 2031)Artificial Intelligence in Agriculture Market Overview
Artificial Intelligence in Agriculture Market (USD Million)
Artificial Intelligence in Agriculture Market was valued at USD 1,638.36 million in the year 2024. The size of this market is expected to increase to USD 6,294.00 million by the year 2031, while growing at a Compounded Annual Growth Rate (CAGR) of 21.2%.
Global Artificial Intelligence in Agriculture Market Growth, Share, Size, Trends and Forecast
*Market size in USD million
CAGR 21.2 %
| Study Period | 2025 - 2031 |
|---|---|
| Base Year | 2024 |
| CAGR (%) | 21.2 % |
| Market Size (2024) | USD 1,638.36 Million |
| Market Size (2031) | USD 6,294.00 Million |
| Market Concentration | Low |
| Report Pages | 363 |
Major Players
- Deere & Company
- IBM
- Microsoft
- The Climate Corporation
- Farmers Edge Inc.
- Granular Inc.
- AgEagle Aerial Systems Inc.
- Descartes Labs, Inc.
Market Concentration
Consolidated - Market dominated by 1 - 5 major players
Global Artificial Intelligence in Agriculture Market
Fragmented - Highly competitive market without dominant players
The Artificial Intelligence in Agriculture Market is rapidly expanding as over 60% of agribusinesses adopt smart systems for crop monitoring, yield prediction, and pest control. These opportunities are boosting demand for AI tools that increase output and drive market expansion. Farmers are implementing machine learning algorithms to optimize resource use and reduce environmental impact. The fast-growing adoption of digital agritech is delivering sustained growth. Agricultural suppliers now provide specialized platforms that combine analytics, automation, and sensor technology.
Innovation Driven by Smart Analytics and Robotics
With more than 57% of providers embedding technological advancements like drone-based imaging, automated soil sensors, and real-time anomaly detection, the market is leveraging advanced hardware-software integration. These innovations are enabling faster decision-making, improved crop health, and reduced losses. The rise of autonomous tractors and AI-powered irrigation is driving operational efficiency. As a result, stakeholders across the agriculture value chain are capturing accelerated growth. Continued te chnical advancements support expanded market expansion.
Opportunities Around Climate-Smart Agriculture
With close to 62% of farms transitioning to climate-optimized systems using AI for water conservation, soil management, and predictive yield control, new opportunities are emerging in sustainable agritech. These innovations foster resilient crop systems and reduce environmental footprint. Governments and suppliers are incentivizing adoption of smart farming tools that promote sustainability. This trend is delivering robust growth in yield quality and resource efficiency. As farms become more climate-aware, the AI agriculture space is set for further expansion.
Future Outlook Centered on Autonomous Farming Platforms
Over 65% of agribusinesses are committing to roll out fully automated planting, harvesting, and supply-chain management systems powered by AI. This future outlook reflects a transformation toward farm systems capable of real-time adaptation and autonomous control. These technological advancements will enable resilient production models, optimized logistics, and enhanced traceability. As farms embrace end-to-end intelligent operations, the result will be sustained market growth and scalable expansion across agri-sectors.
Artificial Intelligence in Agriculture Market Recent Developments
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In July 2024, IBM partnered with agri-tech firms to deploy AI models for weather prediction and yield optimization
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In March 2023, Bayer launched an AI-based platform for real-time crop monitoring to assist farmers in reducing input costs.
Artificial Intelligence in Agriculture Market Segment Analysis
In this report, the Artificial Intelligence in Agriculture Market has been segmented by Technology, Offering, Application, End User, and Geography.
Artificial Intelligence in Agriculture Market, Segmentation by Technology
The Artificial Intelligence in Agriculture Market has been segmented by Technology into Machine Learning, Predictive Analytics, and Computer Vision.
Machine Learning
Machine learning is widely adopted in agriculture for its ability to process vast datasets from sensors and satellites. It helps farmers make data-driven decisions for crop planning and yield optimization. The segment is experiencing rapid growth due to increasing demand for precision agriculture. It continues to evolve with improved algorithmic accuracy and real-time adaptability.
Predictive Analytics
Predictive analytics supports early detection of crop diseases, pest attacks, and resource management. Its ability to forecast outcomes based on historical data enhances farm productivity. The segment is gaining traction with the expansion of smart farming practices. Adoption is particularly strong in regions investing in agritech infrastructure.
Computer Vision
Computer vision applications are revolutionizing tasks such as crop monitoring, weed detection, and quality grading. With advanced image recognition, it enables automated insights from drone or satellite imagery. The segment benefits from rising integration of AI-powered surveillance tools. It plays a critical role in reducing manual labor and improving crop assessment accuracy.
Artificial Intelligence in Agriculture Market, Segmentation by Offering
The Artificial Intelligence in Agriculture Market has been segmented by Offering into Hardware, Software, and Services.
Hardware
Hardware solutions such as sensors, drones, and robotic equipment are foundational to AI deployment in agriculture. They enable real-time data collection across varied terrains. Growth in this segment is driven by increased adoption of automated farming equipment. Hardware continues to evolve with innovations in IoT and edge computing.
Software
Software platforms provide the intelligence layer for processing and analyzing agricultural data. These include AI algorithms, decision support systems, and farm management tools. Demand for software is rising due to the need for scalable and customizable analytics. Cloud-based platforms are making adoption easier across small to mid-size farms.
Services
Consulting, integration, and maintenance services are critical for ensuring successful AI implementation. This segment is expanding with the increase in agritech startups offering specialized support. Services also include training farmers on using AI-driven tools efficiently. The growing need for end-to-end digital transformation is fueling this segment’s relevance.
Artificial Intelligence in Agriculture Market, Segmentation by Application
The Artificial Intelligence in Agriculture Market has been segmented by Application into Precision Farming and Drone Analytics.
Precision Farming
Precision farming leverages AI to optimize planting schedules, irrigation, and fertilization based on data inputs. It enhances resource efficiency and boosts crop yields sustainably. The segment is experiencing significant uptake due to climate-adaptive agriculture. Large-scale farms are increasingly investing in data-driven field monitoring.
Drone Analytics
Drone analytics use aerial imagery and AI to assess crop health, soil conditions, and pest infestations. These tools help in faster and more accurate agronomic decisions. The segment is growing with improved access to affordable UAVs. Regulatory support for drone use in agriculture is further propelling adoption.
Artificial Intelligence in Agriculture Market, Segmentation by End User
The Artificial Intelligence in Agriculture Market has been segmented by End User into Farms, Agro-Tech Companies, Agrochemical Companies, Research Institutes, and Others.
Farms
Farms remain the primary users of AI tools for optimizing crop production and managing labor costs. Both smallholders and commercial farms are integrating AI for resource planning. The segment is driven by rising awareness of the benefits of smart farming technologies. Adoption is high in areas with strong agricultural policy support.
Agro-Tech Companies
Agro-tech companies are major contributors to innovation and product development in AI-driven agriculture. They provide platforms and customized tools tailored to specific farm needs. The segment is scaling fast due to increased venture capital funding. These companies are bridging gaps between traditional farming and advanced digital solutions.
Agrochemical Companies
Agrochemical firms use AI to research and test crop protection chemicals more efficiently. AI helps in predicting soil reaction and chemical effectiveness. This segment benefits from digital farming trends and focus on sustainable agriculture. It is evolving toward eco-friendly solutions through AI modeling.
Research Institutes
Research bodies are leveraging AI for breakthroughs in crop genetics, climate-resilient farming, and automation. They collaborate with governments and tech firms on pilot programs. The segment is instrumental in knowledge dissemination and technology validation. Increased funding for agri-innovation is boosting research activity.
Others
This category includes agribusiness consultants, tech integrators, and educational institutions. Their role is growing in promoting AI literacy and adoption among farmers. The segment supports ecosystem development through training, demo farms, and policy advocacy. It is vital in ensuring long-term scalability of AI solutions.
Artificial Intelligence in Agriculture Market, Segmentation by Geography
In this report, the Artificial Intelligence in Agriculture Market has been segmented by Geography into North America, Europe, Asia Pacific, Middle East & Africa, and Latin America.
Regions and Countries Analyzed in this Report
Artificial Intelligence in Agriculture Market Share (%), by Geographical Region
North America
North America dominates the market with a share of 34%, driven by high technological penetration and strong government support. The presence of major AI companies and advanced infrastructure boosts adoption. Precision farming practices are widespread, especially in the US and Canada. The region continues to invest in smart irrigation and yield forecasting.
Europe
Europe accounts for approximately 26% of the market, with leading initiatives in sustainable farming and agro-robotics. Countries like Germany and the Netherlands are heavily investing in green AI solutions. EU policies promoting digital transformation in agriculture are accelerating growth. Adoption is expanding in both organic and industrial farming segments.
Asia Pacific
Asia Pacific holds about 22% market share and is rapidly emerging due to large agricultural bases and growing food demand. China, India, and Japan are key contributors. Government subsidies and smart village initiatives are boosting AI adoption. The region is seeing major traction in drone-based crop surveillance.
Middle East & Africa
Middle East & Africa represent nearly 10% of the market, with growth centered around climate-resilient technologies. Countries are adopting AI to combat water scarcity and improve food security. Investment in agricultural innovation hubs is driving adoption. The segment is expected to grow as infrastructure improves.
Latin America
Latin America captures around 8% of the market, with Brazil and Argentina leading the charge. AI is used to enhance commodity crop production and reduce dependency on imports. The region is gradually shifting toward digital agriculture through public-private partnerships. Growth is supported by increasing IoT and cloud penetration.
Market Trends
This report provides an in depth analysis of various factors that impact the dynamics of Global Artificial Intelligence in Agriculture Market. These factors include; Market Drivers, Restraints and Opportunities.
Drivers:
- Precision Agriculture Advancements
- Increased Demand for Food Security
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Automation and Labor Shortages - The Global Artificial Intelligence in Agriculture Market is experiencing significant growth, driven primarily by the pressing need for automation and the challenges posed by labor shortages in the agricultural sector. As technology continues to advance, agriculture is increasingly turning to artificial intelligence (AI) solutions to address various inefficiencies and optimize processes throughout the value chain.
One of the primary drivers behind the adoption of AI in agriculture is the urgent need for automation. Traditional farming methods often rely heavily on manual labor, which can be time-consuming, labor-intensive, and prone to human error. As labor costs rise and the availability of skilled agricultural workers declines, farmers are seeking alternative solutions to streamline operations and improve productivity. AI-powered technologies offer a promising solution by automating tasks such as planting, irrigation, crop monitoring, and harvesting, thereby reducing reliance on manual labor and increasing operational efficiency.
The agriculture industry is grappling with labor shortages, exacerbated by factors such as aging farm populations, rural depopulation, and changing demographics. In many regions, finding and retaining skilled agricultural workers has become increasingly challenging, leading to disruptions in farm operations and hampering productivity. AI technologies present an opportunity to mitigate the impact of labor shortages by augmenting human labor with automated solutions. By deploying AI-driven robots, drones, and autonomous machinery, farmers can optimize resource utilization, minimize labor requirements, and maintain productivity levels even in the face of labor scarcity.
Restraints:
- High Initial Investment Costs
- Lack of Infrastructure and Connectivity
- Data Privacy and Security Concerns - The interconnected nature of modern agricultural systems amplifies the risk of data breaches. With AI applications often relying on cloud computing and Internet of Things (IoT) devices, vulnerabilities in these technologies can expose agricultural data to malicious actors. Instances of cyberattacks targeting agricultural organizations have underscored the urgent need for robust cybersecurity measures to safeguard sensitive information.
The regulatory landscape surrounding data privacy and security in agriculture remains fragmented and evolving. Different regions and jurisdictions impose varying requirements and standards, complicating compliance efforts for industry stakeholders operating across multiple markets. Navigating this regulatory maze demands significant resources and expertise, particularly for smaller players lacking the scale and capacity to address compliance challenges effectively.
The collaborative nature of AI-driven agricultural ecosystems introduces complexities in data governance and ownership. As stakeholders share data for collaborative research, predictive analytics, or optimization purposes, questions arise regarding the rights and responsibilities associated with data ownership, access, and usage. Resolving these issues necessitates clear contractual agreements, transparent protocols, and mechanisms for resolving disputes related to data ownership and control.
Opportunities:
- Crop Monitoring and Management
- Sustainable Agriculture Practices
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Market Expansion and Access - One of the key drivers propelling the growth of the AI in agriculture market is the escalating demand for precision farming solutions. Precision agriculture leverages AI algorithms and data analytics to optimize resource utilization, including water, fertilizers, and pesticides, thereby improving crop yields while minimizing environmental impact. This approach enables farmers to make data-driven decisions tailored to specific field conditions, leading to enhanced operational efficiency and cost savings.
The increasing adoption of IoT (Internet of Things) devices and sensors in agriculture is generating vast amounts of data, which can be leveraged to develop advanced AI-powered solutions. These solutions encompass a range of applications, including crop monitoring, yield prediction, pest detection, and soil analysis, enabling farmers to gain deeper insights into their operations and make informed decisions in real-time.
The rising need to mitigate the adverse effects of climate change on agricultural productivity is driving the adoption of AI-driven climate-smart farming practices. AI technologies enable farmers to monitor weather patterns, assess climate risks, and implement adaptive strategies to optimize crop yields and resilience in the face of changing environmental conditions.
Competitive Landscape Analysis
Key players in Global Artificial Intelligence in Agriculture Market include:
- Deere & Company (US)
- IBM (US)
- Microsoft (US)
- The Climate Corporation (US)
- Farmers Edge Inc. (Canada)
- Granular Inc. (Canada)
- AgEagle Aerial Systems Inc. (US)
- Descartes Labs, Inc. (US)
In this report, the profile of each market player provides following information:
- Company Overview and Product Portfolio
- Key Developments
- Financial Overview
- Strategies
- Company SWOT Analysis
- Introduction
- Research Objectives and Assumptions
- Research Methodology
- Abbreviations
- Market Definition & Study Scope
- Executive Summary
- Market Snapshot, By Technology
- Market Snapshot, By Offering
- Market Snapshot, By Application
- Market Snapshot, By End User
- Market Snapshot, By Region
- Artificial Intelligence in Agriculture Market Dynamics
- Drivers, Restraints and Opportunities
- Drivers
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Precision Agriculture Advancements
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Increased Demand for Food Security
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Automation and Labor Shortages
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- Restraints
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High Initial Investment Costs
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Lack of Infrastructure and Connectivity
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Data Privacy and Security Concerns
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- Opportunities
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Crop Monitoring and Management
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Sustainable Agriculture Practices
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Market Expansion and Access
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- Drivers
- PEST Analysis
- Political Analysis
- Economic Analysis
- Social Analysis
- Technological Analysis
- Porter's Analysis
- Bargaining Power of Suppliers
- Bargaining Power of Buyers
- Threat of Substitutes
- Threat of New Entrants
- Competitive Rivalry
- Drivers, Restraints and Opportunities
- Market Segmentation
- Artificial Intelligence in Agriculture Market, By Technology, 2021 - 2031 (USD Million)
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Machine Learning
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Predictive Analytics
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Computer Vision
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- Artificial Intelligence in Agriculture Market, By Offering, 2021 - 2031 (USD Million)
- Hardware
- Software
- Service
- Artificial Intelligence in Agriculture Market, By Application, 2021 - 2031 (USD Million)
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Precision Farming
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Drone Analytics
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Artificial Intelligence in Agriculture Market, By End User, 2021 - 2031 (USD Million)
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Farms
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Agro-Tech Companies
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Agrochemical Companies
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Research Institutes
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Others
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- Artificial Intelligence in Agriculture Market, By Geography, 2021 - 2031 (USD Million)
- North America
- United States
- Canada
- Europe
- Germany
- United Kingdom
- France
- Italy
- Spain
- Nordic
- Benelux
- Rest of Europe
- Asia Pacific
- Japan
- China
- India
- Australia & New Zealand
- South Korea
- ASEAN (Association of South East Asian Countries)
- Rest of Asia Pacific
- Middle East & Africa
- GCC
- Israel
- South Africa
- Rest of Middle East & Africa
- Latin America
- Brazil
- Mexico
- Argentina
- Rest of Latin America
- North America
- Artificial Intelligence in Agriculture Market, By Technology, 2021 - 2031 (USD Million)
- Competitive Landscape
- Company Profiles
- Deere & Company (US)
- IBM (US)
- Microsoft (US)
- The Climate Corporation (US)
- Farmers Edge Inc. (Canada)
- Granular Inc. (Canada)
- AgEagle Aerial Systems Inc. (US)
- Descartes Labs, Inc. (US)
- Company Profiles
- Analyst Views
- Future Outlook of the Market