Artificial Intelligence in Agriculture Market Size, Share, Growth, Trends, and Global Industry Analysis: By Components (Solution, Service), By Technology (Machine Learning, Computer Vision, Predictive Analysis), By Application (Crop & Soil Monitoring, Livestock Heath Monitoring, Intelligent Spraying, precision farming, Agriculture Robot, Weather Data & Forecast, Others), By Deployment Mode (Cloud Based, on-Premises), By Farm Size (Small farms, Mid-sized Farm, Large Firm), and Region Forecast 2020-2031

Artificial Intelligence in Agriculture Market size was valued at US$ 2,350 million in 2024 and is expected to reach US$ 4,980 million by 2031, growing at a significant CAGR of 12.5% from 2025-2031. Moreover, the U.S. Artificial Intelligence in Agriculture Market is projected to grow significantly, reaching an estimated value of US$ 1,750 million by 2031. The market refers to the application of advanced AI technologies including machine learning, computer vision, predictive analytics, and robotics to improve farming processes, crop management, and agricultural decision-making. This market covers AI-enabled tools and solutions that enhance yield, optimize resource use, monitor crop health, and automate repetitive tasks across various agricultural activities.

The market is rapidly gaining traction as farmers and agribusinesses seek to address growing global food demands, labor shortages, and climate-related challenges. AI-driven solutions enable precision farming, real-time monitoring, and data-driven insights, leading to higher efficiency and sustainability. As adoption rises, the market is witnessing strong investment from technology providers and agriculture equipment manufacturers. In particular, the U.S. and Asia Pacific regions are emerging as key growth hubs due to advanced infrastructure and supportive government initiatives. Overall, AI in agriculture is evolving from an experimental niche to a critical enabler of next-generation farming practices worldwide.

Facts & Figures

• AI Adoption is Most Robust in Yield-Sensitive Crops: Grapes, tomatoes, and lettuce crops are seeing a quicker adoption of AI due to weather variability and disease infection susceptibility, where real-time monitoring provides immediate ROI.
• Computer Vision Is Revolutionizing Crop Management: Deep imaging and AI now facilitate real-time, plant-level decisions, allowing activities such as selective weeding, ripeness detection, and disease separation with precision surgery.
• Smallholder Farms Are Fueling Localization Trend: Demand for light, modular AI solutions for non-industrial farms is surging, particularly in Asia and Africa, where conventional systems are beyond budget or overly complex.
• Agri-Tech Startups Are Scaling with Intelligent Capital: Venture capital is pouring into AI-based agricultural platforms with progressively larger amounts, an indication of positive investor faith in the sector's potential to enhance productivity and food security at the same time.

Key Developments:

• In January 2025, John Deere introduced the second-generation 9RX tractor with onboard AI autonomy kits and vision systems to address labor shortages through autonomous navigation. The company also expanded its Startup Collaborator program, collaborating with six deep-tech start-ups developing AI-based technologies like 3D Earth imagery and wireless charging.
• In March 2024, Malaysia-based Agroz Group further advanced its AI-based "Copilot for Farmers" platform using Microsoft Cloud and AI solutions to optimize vertical farming. The platform integrates IoT, automation, 5G, and edge computing to deliver pesticide-free, nutrient-rich crops daily under precision-controlled environments.
• In March 2024, Bayer piloted a generative AI platform developed by Microsoft that assists farmers with real-time data-driven agronomic recommendations via natural language search, accelerating decision-making and increasing productivity in real time.

Artificial Intelligence in Agriculture Market Segmentation:

Based on the components:

• Solutions
• Services

Solutions are the technological backbone of AI in agriculture, providing farmers with intelligent decision-making systems, automated tasks, and real-time monitoring. Combined platforms that integrate machine learning, sensor data, and analytics dashboards are coming to the fore with maximum traction among them. They offer actionable insights, like irrigation scheduling, pest risk thresholds, and yield prediction, based on real-time field-level data. Their modular nature allows them to scale from smallholder farms to industrial-scale farms, and they are thus highly adaptable. With climate resilience gaining strategic prominence, AI-driven solutions are increasingly being seen as operational necessities for precision agriculture, allowing agribusinesses to move away from reactive to predictive crop care.

Based on the Technology:

• Machine Learning
• Computer Vision
• Predictive Analysis

Machine learning is the most widely used AI technology in agriculture, powering predictive intelligence and automation through nearly every aspect of the ag cycle. Because it can learn from historical and current data, it's particularly well-suited to estimating crop disease, predicting yield, and optimizing input use. ML algorithms scan satellite imagery, soil health metrics, and weather patterns to identify latent patterns invisible to the human eye. They become smarter with time, allowing hyper-localized decisions like where to irrigate or how much pesticide to apply. What is machine learning in this case is that it is adaptive, equally applicable to large mechanized farms and scattered small holdings by tailoring the knowledge to the specifics of the farm.

Based on the application:

• Crop & Soil Monitoring
• Livestock Health Monitoring
• Intelligent Spraying
• Precision farming
• Agriculture Robots
• Weather Data & Forecasting

Precision farming is where AI’s impact is most visibly transformative, enabling farmers to treat individual plants or micro-sections of land rather than entire fields uniformly. Through tools like AI-guided tractors, multispectral imaging drones, and in-field sensors, precision farming delivers data-backed strategies for nutrient management, seeding density, and harvesting schedules. The focus isn’t just on maximizing yield but on improving efficiency, minimizing fertilizer use, reducing water consumption, and preventing over-application of chemicals. This technology is especially essential in addressing environmental problems and sustainability goals. With mounting pressure on land and natural resources, precision farming based on AI has moved from testing to ensuring long-term crop profitability and productivity.

Based on the deployment mode:

• Cloud based
• On-Premises

Cloud-based deployment is becoming the standard for AI in agriculture, with scalability, access, and real-time processing offered without requiring local infrastructure. It allows farms of any size to access high-end analytics, modeling features, and remote monitoring functionality through mobile devices and low-power IoT nodes. It brings AI to everyone's doorsteps, making valuable insights available even in remote locations with little technical capability. Updates, model training, and connectivity to APIs or satellite feeds are done off-farm, so farmers can maintain a low overhead on maintenance. The adaptability of cloud infrastructure also enables cooperation between farmers, agronomists, and agribusinesses, enabling joint decision-making. As farming becomes more data-driven, cloud platforms are emerging as key infrastructure for digital-first, responsive farm economies.

Based on the farm size:

• Small farm
• Mid-Sized farm
• Large farm

Large farms are the first to adopt AI because they can absorb the upfront cost of technology investments and dissipate gains in efficiency over extensive areas. These farms typically utilize autonomous machinery, swarms of drones, and machine learning-powered crop monitoring to reduce dependence on labor and optimize use of resources. With more points of information and set target operating goals, large farms can optimize the utilization of machine learning models for yield forecasting, pest detection, and supply chain management. AI helps bring together decision-making from distant points so that consistent quality is achieved and operation lag is reduced. For large agribusinesses, AI is no longer a competitive advantage, it's becoming an underlying strategy to drive margins, increase production, and meet evolving needs for sustainability compliance at scale.