United States AI in Agriculture Market By Technology (Machine Learning, Predictive Analytics and Computer Vision), By Offering (Hardware, Software and AI-As-A-Service), By Application (Precision Farming, Livestock Monitoring, Agriculture Robots, Drone and Others), By Region, Competition, Forecast & Opportunities, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 369.28 Million
CAGR (2026-2031)	13.39%
Fastest Growing Segment	Predictive Analytics
Largest Market	Midwest
Market Size (2031)	USD 784.88 Million

Market Overview

The United States AI in Agriculture Market will grow from USD 369.28 Million in 2025 to USD 784.88 Million by 2031 at a 13.39% CAGR. The United States AI in Agriculture market comprises the deployment of machine learning algorithms, computer vision systems, and autonomous robotics to optimize farming operations such as crop monitoring and predictive yield analysis. The market is primarily supported by the critical necessity to mitigate chronic labor shortages through automation and the escalating demand for enhanced production efficiency to manage diminishing arable land, which serve as fundamental growth drivers rather than temporary trends.

However, a significant challenge impeding broader market expansion is the substantial capital investment required for implementation, which creates a barrier to entry for smaller farming operations. This economic disparity limits the universal scalability of advanced digital solutions across the sector. According to the U.S. Department of Agriculture, in 2024, 68 percent of large-scale crop farms utilized precision agriculture technologies, indicating a substantial adoption gap compared to smaller enterprises with limited resources.

Key Market Drivers

Escalating Agricultural Labor Shortages and Rising Workforce Costs represent the primary catalyst propelling the United States AI in Agriculture market. As domestic workforce availability dwindles, producers are compelled to transition from manual dependency to autonomous systems to maintain operational continuity. This economic pressure is intensified by the rapid appreciation of wage rates, which has transformed automation from a luxury into a financial necessity for solvency. According to the American Farm Bureau Federation, May 2025, in the 'Market Intel' report, labor costs were forecast to reach over $53 billion across the agricultural industry in 2025, underscoring the urgent financial incentive for farms to integrate labor-saving AI technologies.

Surging Adoption of Precision Farming for Yield Maximization further accelerates market growth by leveraging computer vision and machine learning to optimize input application. This technological shift allows efficient resource management, reducing chemical expenditure while enhancing crop consistency through real-time field analysis. According to CropLife and Purdue University, July 2025, in the 'Precision Agriculture Dealership Survey', machine vision weed detection is currently offered by 4 percent of dealers, with 19 percent planning to add the service in the next three years, indicating a robust trajectory for AI implementation. This sector-wide pivot toward digitalization is heavily supported by capital inflows; according to AgFunder, March 2025, in the 'Global AgriFoodTech Investment Report 2025', United States startups garnered $6.6 billion of all capital raised in 2024, fueling the development of these advanced solutions.

Download Free Sample Report

Key Market Challenges

The substantial capital investment required for deployment constitutes a primary obstacle to the expansion of the United States AI in agriculture market. This financial barrier prevents smaller and mid-sized farming operations from integrating necessary hardware and software systems, effectively concentrating market share within a limited tier of large-scale enterprises. Consequently, the market struggles to achieve universal scalability as the high initial costs often outweigh the immediate financial capacity of a significant portion of the agricultural sector. This economic pressure restricts the addressable customer base and slows the adoption rate of automated solutions across the broader industry.

The impact of this financial strain is evident in recent industry economic indicators regarding operator liquidity. According to the American Farm Bureau Federation, in 2024, net farm income was projected to decrease by nearly 26 percent from the previous year due to elevated production expenses and lower commodity prices. This reduction in available working capital directly impedes the ability of agricultural producers to fund high-value technology upgrades. Without accessible financial entry points, the market faces stunted growth rates as potential buyers prioritize operational solvency over digital transformation investments.

Key Market Trends

The Integration of Generative AI for Agronomic Decision Support is fundamentally reshaping farm management by transitioning from passive data monitoring to active, agentic advisory systems. Unlike traditional predictive models that analyze single data streams, these advanced generative models synthesize multi-modal inputs—including satellite imagery, machinery telematics, and historical weather patterns—to provide context-aware, actionable recommendations for crop protection. This capability allows agronomists and growers to instantly interpret complex field conditions and receive tailored remediation strategies, significantly reducing the time between issue detection and intervention. According to Taranis, July 2024, in the 'Taranis Launches Ag Assistant™ to Transform Farm Decision Making' press release, their generative AI model utilizes a proprietary training library of more than 500 million leaf-level data points collected across the United States to deliver these high-precision, real-time agronomic insights.

The Advancement of Green-on-Green Computer Vision for Precision Weeding represents a critical technological leap, enabling autonomous systems to distinguish weeds from crops within complex canopies rather than simply detecting plant matter against soil. This granular distinction allows for precise, targeted herbicide application that drastically lowers chemical volatility and operational costs, moving beyond the blanket spraying approaches of the past. As environmental regulations tighten and input prices remain volatile, this capability is becoming essential for maintaining profitability while adhering to sustainability mandates. According to John Deere, September 2024, in the 'See & Spray Customers See 59% Average Herbicide Savings in 2024' report, the utilization of this AI-powered green-on-green technology resulted in an average herbicide savings of 59 percent across corn, soybean, and cotton fields in the United States during the 2024 growing season.

Segmental Insights

The Predictive Analytics segment currently stands as the most rapidly expanding area within the United States AI in Agriculture Market. This growth is primarily driven by the increasing necessity for farmers to mitigate environmental risks and optimize resource allocation through data-driven insights. By analyzing historical trends and real-time variables, these tools allow producers to forecast crop yields and weather patterns with greater accuracy. Furthermore, initiatives by the United States Department of Agriculture to promote precision farming practices have accelerated the adoption of these analytical solutions, ensuring sustainable productivity and operational efficiency across the sector.

Regional Insights

The Midwest leads the United States AI in agriculture market due to its extensive concentration of large-scale crop production, particularly within the Corn Belt. This dominance is supported by the presence of major agricultural machinery manufacturers, such as John Deere in Illinois, which drives the adoption of automated equipment and precision farming technologies. Furthermore, strong collaboration between technology developers and land-grant universities fosters the practical application of data analytics in field operations. These factors collectively establish a favorable environment for implementing artificial intelligence to improve operational efficiency and resource management across the region.

Recent Developments

• In October 2024, Carbon Robotics secured $70 million in Series D funding to accelerate the expansion of its artificial intelligence-powered agricultural solutions. The investment round was led by BOND and included participation from the venture capital arm of a major graphics processing unit manufacturer. The Seattle-based company announced plans to utilize the capital to scale the production of its LaserWeeder, a robotic implement that uses deep learning and computer vision to identify and eliminate weeds with high-precision lasers. Additionally, the funding was designated to support the research and launch of new software and hardware products, further advancing chemical-free weed control methods in the United States agriculture market.
• In June 2024, CNH Industrial entered a strategic partnership with Bluewhite, a technology firm specializing in autonomous farming solutions, to distribute aftermarket autonomy kits. This collaboration enabled select New Holland dealers in the western United States to sell, distribute, and service Bluewhite’s AI-driven systems, which can transform existing tractors into fully autonomous machines. The technology utilizes computer vision and artificial intelligence to execute tasks such as spraying and disking without a human operator in the cab. By integrating these scalable autonomous solutions, the company aimed to help specialty crop farmers address critical labor shortages and reduce operational costs through advanced automation.
• In April 2024, AGCO Corporation and Trimble completed their joint venture transaction to form PTx Trimble, a new entity focused on the mixed-fleet precision agriculture sector. AGCO acquired an 85% stake in the venture for $2.0 billion, combining Trimble’s agricultural business with AGCO’s JCA Technologies. This strategic collaboration aimed to deliver factory-fit and retrofit autonomy and guidance solutions that are compatible with various equipment brands. The partnership was designed to accelerate the development of next-generation technologies, including autonomous steering and data management systems, thereby enhancing productivity and sustainability for farmers across the United States and global markets.
• In January 2024, Doosan Bobcat unveiled the industry’s first autonomous and electric articulating tractor, the AT450X, during a major technology exhibition in Las Vegas. Developed through a collaboration with Agtonomy, a Silicon Valley-based agricultural software company, the machine integrates advanced artificial intelligence to perform labor-intensive tasks such as mowing and spraying in vineyards and orchards. The tractor utilizes Agtonomy’s TeleFarmer technology, which combines software and embedded computing to enable emissions-free, autonomous operation. Farmers can direct the equipment’s mission path remotely via a mobile application, allowing for increased efficiency and precision in managing specialty crops within the United States agriculture market.

Key Market Players

• International Business Machines Corporation (IBM)
• Granular, Inc.
• Microsoft
• Deere & Company
• Awhere Inc.
• Climate LLC.
• Agribotix, LLC
• Descartes Labs Inc.
• Valmont Industries, Inc.

By Technology	By Offering	By Application	By Region
Machine Learning Predictive Analytics Computer Vision	Hardware Software AI-As-A-Service	Precision Farming Livestock Monitoring Agriculture Robots Drone Others	Northeast Midwest South West

Report Scope:

In this report, the United States AI in Agriculture Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• United States AI in Agriculture Market, By Technology:

• Machine Learning
• Predictive Analytics
• Computer Vision

• United States AI in Agriculture Market, By Offering:

• Hardware
• Software
• AI-As-A-Service

• United States AI in Agriculture Market, By Application:

• Precision Farming
• Livestock Monitoring
• Agriculture Robots
• Drone
• Others

• United States AI in Agriculture Market, By Region:

• Northeast
• Midwest
• South
• West