Global Artificial Intelligence in Agriculture Market Size, Trend & Opportunity Analysis Report, by Technology (Machine Learning, Computer Vision, Predictive Analytics), Offering (Software, AI-as-a-Service), Application (Drone Analytics, Precision Farming,

Market Definition and Introduction

The global artificial intelligence in agriculture market value is expected to attain USD 2.09 billion by 2024, and it is projected to surge to USD 20.56 billion by 2035, thereby having a robust CAGR of 23.56% during the forecast period (2025-2035). The awesomely unprecedented digital transformation adaptation in the agricultural sector has spearheaded the integration of AI technologies into the strategies that are likely to boost productivity and decrease risks, and ensure food security under conditions of climate volatility and resource constraints. Truly, AI's capacity for copious data processing from sensors, drones, and satellite imagery has brought about a revolutionary change in how farmers make decisions, transforming their age-old practices into data-driven, precise operations.

Artificial intelligence plays a key role in ensuring that almost every aspect of the farming life cycle is optimised, from crop monitoring and yield predictions to pest detection and precision irrigation. Machine learning, computer vision, and predictive analytics have advanced agricultural stakeholders from acting reactively to taking proactive measures instead. By turning raw agricultural data into actionable intelligence, AI has been directing stakeholders to reduce operational costs, improve yield, and achieve sustainable management of environmental resources. The emerging trend of drone analytics, AI-powered software, and predictive crop models that fine-tune every input across diverse agro-climatic conditions is evidence of such a transformation.

AI-as-a-Service (AIaaS) modules reduce the entry barrier of semi-small farms by adopting cost-effective, scalable, cloud-based solutions that democratize access to advanced tools. Demand is incited through food production imperatives but is also driven by government incentives, sustainability edicts, and global interest in agri-tech innovation. In the future, smart farming technologies will continue to penetrate the agricultural ecosystem. Artificial intelligence is then poised to become the linchpin of modern agri-business strategies.

Recent Developments in the Industry

In March 2024, IBM Corporation launched a new AI-powered crop monitoring platform that combines satellite imagery and weather analytics to deliver real-time yield optimisation recommendations to farmers across North America and Europe.

In January 2024, Deere & Company unveiled an upgraded version of its See & Spray™ technology, which uses computer vision to detect and treat weeds with surgical precision, significantly reducing herbicide usage.

In October 2023, Microsoft Corporation collaborated with Bayer CropScience to co-develop an AI-based sustainable farming dashboard that uses IoT sensors and machine learning models to enhance soil health monitoring and fertiliser application efficiency.

In August 2023, Israeli agri-tech startup Taranis secured $40 million in Series C funding to scale its aerial intelligence platform, which uses computer vision and drone imagery to diagnose crop health issues at the leaf level.

Market Dynamics

Wave-Like Advancement of AI Technologies Crippled by Shortage of Agri-Labour and Merciless Forces of Climate Change

Labour shortages and erratic weather patterns are some of the most pressing issues that plague the world's farming community today, and that is where AI comes in. Predictive analytics and autonomous systems-in short, AI-are being used to tackle labour shortages and predict critical events like pest infestations and irrigation requirements. This translates into building smarter and more resilient agricultural operations that withstand the ravishes of climate extremes while utilising resources efficiently.

Government Is Supporting Technology Through Smart Farming Initiatives

Policymakers and agricultural support systems across the world view AI as a disruptive technology in agriculture on a global scale and are investing in programs that promote ease of adoption. From India's National Agri-Tech Infrastructure to the EU's precision farming subsidy, these initiatives expand the enabling environment for technology adoption. Moreover, strategic investments in rural connectivity and digital literacy are fast-tracking adoption, especially in developing regions.

Increased Funding from Agri-Tech Startups and Strategic Partnerships with Corporations

Venture capital funding and cross-industry partnerships have sprung up in the agri-tech ecosystem. Alliances between leading tech companies and traditional agricultural giants are being formed to co-develop AI solutions that solve real-world problems in the field. This innovation momentum is bridging digital capabilities to their ground applications, which ensures a sleek integration of the technology and wider market acceptance.

AI-as-a-Service Model Offering Small-to-Medium Enterprises (SMEs) Affordable Access

AI-as-a-Service marketplaces lower economic and technological barriers for smallholder farmers. Subscription-based services, often hosted in a cloud ecosystem, provide real-time decision support without heavy infrastructure investments. From mobile apps that provide local planting advice to AI-based pest alerts via SMS, SMEs have now been empowered to use high-tech tools with the least overhead burden.

AI and the IoT, and Drone Integration Revolutionising Precision Farming

In its intersection with IoT technology and drones, AI is shaping the next generation of precision agriculture. Farmers may now make targeted action plans using data from multispectral images, soil moisture sensors, and climate monitors, following a systems approach that allows for precise fertiliser application, early intervention against diseases, and timely harvest, giving more yields with less waste for sustainable development.

Attractive Opportunities in the Market

Explosive Rise in Smart Farming – Adoption of AI-enabled farm management systems improves yield predictability and operational efficiency.
Drone Analytics Expansion – High-resolution aerial imagery revolutionises crop monitoring and pest detection.
AI-as-a-Service Surge – Subscription-based AI platforms empower smallholder farmers with predictive insights.
Computer Vision for Crop Health – Deep learning models offer real-time disease detection at leaf-level accuracy.
Precision Irrigation & Resource Optimisation – Machine learning enhances water use efficiency and fertiliser application.
Yield Forecasting Tools – Predictive models aid agribusinesses in supply chain planning and market readiness.
Government-Led Digital Farming Policies – National initiatives support rural AI deployment and data infrastructure.
Agri-Robotics Integration – Automation of seeding, weeding, and harvesting drives labour efficiency.
Sustainable Agriculture Goals – AI supports carbon-neutral farming and climate-smart crop strategies.
Global Food Security Initiatives – AI-enabled agriculture addresses rising demand amid shrinking arable land.

Report Segmentation

By Technology: Machine Learning, Computer Vision, Predictive Analytics

By Offering: Software, AI-as-a-Service

By Application: Drone Analytics, Precision Farming, Agriculture Robots, Livestock Monitoring, Labour Management, Others

By Component:
Hardware, Software, Service

By Region: North America (U.S., Canada, Mexico), Europe (UK, Germany, France, Spain, Italy, Spain, Rest of Europe), Asia-Pacific (China, India, Japan, Australia, South Korea, Rest of Asia-Pacific), LAMEA (Brazil, Argentina, UAE, Saudi Arabia (KSA), Africa Rest of Latin America)

Key Market Players

IBM Corporation, Microsoft Corporation, Deere & Company, Trimble Inc., Bayer CropScience, Raven Industries, Prospera Technologies, AgEagle Aerial Systems, Taranis, Granular, Inc.

Report Aspects

Base Year: 2024
Historic Years: 2022, 2023, 2024
Forecast Period: 2025-2035
Report Pages: 293

Dominating Segments

Software Segment Dominates Market on High Adoption of Smart Farm Management Tools

The leading software segment of the artificial intelligence in agriculture markets has been rightfully regarded as the backbone of precision farming and crop forecasting applications. These software applications embedded with machine-learning and predictive algorithms provide end-to-end farm management solutions from seeding schedules to market predictions. With a whole host of mobile application-based software and easy-to-use dashboards in place, adoption has spread across varied geographies.

AI-as-a-Service Tools Come Into Favour by Providing Inexpensive and Scalable Intelligence Mechanisms

AI-as-a-Service is on a growth trajectory, given that agribusinesses need scalable cloud solutions with low entry costs. This segment offers modular services in pest forecasting, weather modelling, and inventory optimisation to diverse stakeholders from agri-input suppliers to contract farmers. In this way, these plug-and-play solutions help democratize AI access in both developed and emerging markets.

Drone Analytics Revolutionising Field Data Collection and Crop Monitoring

Drone analytics has turned out to be the new revolution in agriculture, allowing unmatched accuracy and consistency in field scouting and crop health analysis. With the help of onboard AI image recognition software, the drones can detect anomalies, estimate yield potential, and monitor extensive fields within minutes. The merging of drone analytics with farm management systems is enabling real-time interventions, resulting in augmented productivity and sustainability.

Precision Farming Adoption Increasing Through Real-Time Data-Driven Decision Making

Precision farming has redefined the long-standing traditional customs of agriculture with respect to data-driven decision-making at each stage. Be it variable rate planting or targeted fertilisation, AI puts power in the hands of farmers through very granular insights that vastly enhance input-output ratios. Decreasing sizes of landholdings coupled with growing resource scarcity make AI-supported precision agriculture a strategic choice to optimise yield against input costs.

Key Takeaways

AI Revolutionises Farming – AI-driven platforms empower decision-making and optimise crop yields.
Software Solutions Lead – End-to-end farm management systems dominate due to analytics capabilities.
Drone Tech Flourishes – Drone-based crop imaging enhances surveillance and precision input use.
Scalable AI-as-a-Service Grows – Cloud-based solutions democratize access to advanced agri-tools.
Predictive Intelligence Shapes Farming – Machine learning delivers forecasts on pests, weather, and yield.
Sustainable Practices Gain Traction – AI facilitates eco-friendly, resource-efficient farming.
Startups and Giants Collaborate – Strategic alliances accelerate tech adoption in agri-ecosystems.
Asia-Pacific Leads Agri-Tech Investment – Government-backed initiatives drive rapid AI adoption.
Digital Ecosystems Evolve – Integration with IoT and robotics reshapes agri-value chains.
Precision Agriculture Becomes Mainstream – Hyper-local interventions increase ROI for farms of all sizes.

Regional Insights

Artificial Intelligence in Agriculture Market Boosted by a Strong Ecosystem of Agri-Tech in North America

North America is the biggest market for AI in agriculture because of the early adoption of precision farming technology and a very strong digital infrastructure. Innovations in the AI-led agriculture system are driven in the U.S. with the combination of venture-based agri-tech startups and traditional machinery firms specialising in integrating AI into agricultural functional features. The existence of sophisticated supply chains and high investments in drone and robotics research further bolsters its leadership status in the area.

Europe Follows AI-for-agriculture Adoption Driven by Sustainability Goals

Europe reports phenomenal increases in AI-as-in agriculture with the sustainability imperatives of the European Union and the promotion of smart farms under the Common Agricultural Policy (CAP). Innovating pilot programs instituted in countries such as Germany, France, and the Netherlands focus on soil health, water-use efficiency, and biodiversity preservation, all powered through AI analytics.

Asia-Pacific Will Be Fastest Fastest-Growing Market in AI for Agriculture

Asia-Pacific is all set to have the fastest growth due to an increase in investments in agri-tech infrastructure and also in food security requirements for a growing population. A new aspect to be seen is that both India and China are providing huge investments in digital farming ecosystems through public-private partnerships. Rural digitisation, backed by the government and the rising number of mobile-enabled farming applications, will revolutionise traditional methods of farming across the region.

Latin America and the Middle East, and Africa on the Strategic Deployment of AI

In this way, Latin America and MEA are introducing AI tools progressively to enhance productivity and answer regional farming problems. For instance, Brazil works through predictive analysis to offset the effects of climate change on soybean production. Similar countries across Africa are utilising mobile AI platforms for pest diagnostics and yield forecasting, thus enhancing rural livelihoods.

Core Strategic Questions Answered in This Report

Q. What is the expected growth trajectory of the artificial intelligence in agriculture market from 2024 to 2035?

The global artificial intelligence in agriculture market is projected to expand from USD 2.09 billion in 2024 to USD 20.56 billion by 2035, reflecting a CAGR of 23.56%. This growth is driven by the rise of precision farming, increasing food demand, and the integration of AI with IoT, drones, and smart farming practices.

Q. Which key factors are fuelling the growth of the artificial intelligence in agriculture market?

Key growth factors include increasing demand for yield optimisation, government initiatives supporting smart agriculture, reduced labour availability, technological advancements such as drone imaging and computer vision, and the growing influence of agri-tech startups. The scalability offered by AI-as-a-Service models is also accelerating global adoption.

Q. What are the primary challenges hindering the growth of artificial intelligence in the agriculture market?

Major hurdles include the high initial cost of AI-enabled infrastructure, lack of digital literacy among rural farmers, fragmented data sources, inconsistent internet connectivity in remote areas, and the need for customised regional datasets to train AI models effectively across diverse geographies.

Q. Which regions currently lead the artificial intelligence in agriculture market in terms of market share?

North America leads in market share due to its advanced digital infrastructure and strong R&D capabilities, followed by Europe with robust sustainability frameworks and AI-friendly policies. Asia-Pacific is gaining momentum as the fastest-growing region due to increasing investment and policy support for agri-tech.

Q. What emerging opportunities are anticipated in the artificial intelligence in agriculture market?

Emerging opportunities include the proliferation of AI-driven crop diagnostics, integration of robotics for field automation, growth in AIaaS platforms for rural farmers, the rise of predictive crop modelling for climate adaptation, and expanding use of drone-based analytics for large-scale monitoring.

Key Benefits for Stakeholders

The report offers a quantitative assessment of market segments, emerging trends, projections, and market dynamics for the period 2024 to 2035.
The report presents comprehensive market research, including insights into key growth drivers, challenges, and potential opportunities.
Porter's Five Forces analysis evaluates the influence of buyers and suppliers, helping stakeholders make strategic, profit-driven decisions and strengthen their supplier-buyer relationships.
A detailed examination of market segmentation helps identify existing and emerging opportunities.
Key countries within each region are analysed based on their revenue contributions to the overall market.
The positioning of market players enables effective benchmarking and provides clarity on their current standing within the industry.
The report covers regional and global market trends, major players, key segments, application areas, and strategies for market expansion. Show more