Applied AI in Agriculture Market Report 2026

The applied ai in agriculture market size has grown exponentially in recent years. It will grow from $3.75 billion in 2025 to $4.86 billion in 2026 at a compound annual growth rate (CAGR) of 29.6%. The growth in the historic period can be attributed to early adoption of basic automation in farming, growing need for crop monitoring efficiency, increasing agricultural data generation from sensors, demand for reduced manual labor dependency, emergence of precision farming concepts.

The applied ai in agriculture market size is expected to see exponential growth in the next few years. It will grow to $13.57 billion in 2030 at a compound annual growth rate (CAGR) of 29.3%. The growth in the forecast period can be attributed to rising use of AI-enabled predictive analytics, increasing deployment of autonomous agricultural machinery, growing investment in digital farm platforms, expansion of ai-driven livestock monitoring, demand for sustainable farming supported by ai solutions. Major trends in the forecast period include growing adoption of AI-based crop and soil health assessment, expansion of data-driven farm decision-making practices, rising integration of automation in large-scale farming operations, increasing demand for real-time agricultural monitoring solutions, greater focus on ai-supported yield enhancement techniques.

The growing crop productivity is expected to drive the expansion of the applied AI in agriculture market moving forward. Crop productivity refers to the output of crops, typically measured in terms of yield per unit area of land. This is increasing due to advancements in agricultural technologies and practices, such as the development of improved crop varieties, more efficient irrigation methods, and the adoption of precision farming techniques. Applied AI in agriculture contributes to higher crop productivity by optimizing farming practices with data-driven insights, predictive analytics, and automation. For example, in January 2024, the United States Department of Agriculture (USDA) reported that the average yield for U.S. rice in 2023 was estimated at 7,649 pounds per acre, an increase of 264 pounds from the 2022 average yield of 7,385 pounds per acre. As a result, the rise in crop productivity is fueling the growth of the applied AI in agriculture market.

Key players in the applied AI in agriculture market are focusing on developing innovative solutions, such as AI-based tools, to maintain their market position. These AI-based tools are software and technologies that leverage artificial intelligence to improve various aspects of farming and agricultural practices. For example, in July 2024, Google LLC, a US-based technology company, introduced an AI-based tool called Agricultural Landscape Understanding (ALU) to enhance agricultural practices in India, with a focus on drought preparedness and irrigation management. The ALU tool uses high-resolution satellite imagery and machine learning to offer tailored insights for individual farm fields, addressing the diverse needs of India's agricultural landscape. By defining clear field boundaries, the tool analyzes factors such as crop type, field size, and proximity to water sources, which are vital for effective irrigation and drought management strategies. This initiative aims to empower farmers by improving crop yields, facilitating access to capital, and enhancing market access for agricultural products.

In August 2023, PTx Trimble, a US-based farming company, acquired Bilberry for an undisclosed amount. This acquisition is intended to boost Trimble's precision agriculture capabilities, particularly in selective spraying technologies. Bilberry, also a US-based company, specializes in AI-driven weed recognition systems that enable precise herbicide application.

Major companies operating in the applied ai in agriculture market are Microsoft Corporation, BASF SE, International Business Machines Corporation, Bayer AG, Deere & Company, SAP SE, CNH Industrial N.V., Kubota Corporation, Corteva Inc., AGCO Corporation, Trimble Inc., Raven Industries Inc., The Climate Corporation, AG Leader Technology, The BAE Systems Taranis, Farmers Edge Inc., PrecisionHawk, AgEagle Aerial Systems, Descartes Labs Inc., Prospera Technologies Ltd., Agribotix, Gamaya.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

Tariffs on hardware components such as sensors, drones, robotics, and IoT devices are increasing procurement costs for AI-enabled agricultural systems, impacting adoption rates across precision farming, drone analytics, and livestock monitoring. Regions dependent on imported technology particularly Asia-Pacific, Europe, and Latin America are experiencing higher deployment costs, while domestic manufacturers in North America and parts of Asia benefit from reduced foreign competition. Although tariffs raise barriers for technology integration, they also encourage local production, innovation, and value-chain development within agricultural AI ecosystems.

The applied AI in agriculture market research report is one of a series of new reports that provides applied AI in agriculture market statistics, including applied AI in agriculture industry global market size, regional shares, competitors with a applied AI in agriculture market share, detailed applied AI in agriculture market segments, market trends and opportunities, and any further data you may need to thrive in the applied AI in agriculture industry. This applied AI in agriculture market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Applied artificial intelligence (AI) in agriculture involves using AI technologies to optimize and enhance various farming practices. This includes employing AI-driven tools and systems to analyze data, automate tasks, and provide actionable insights that improve decision-making and efficiency in agriculture.

The main components of applied AI in agriculture are hardware, software, and services. Hardware refers to the physical devices and equipment used to deploy AI technologies, such as sensors, drones, cameras, and other machinery that collect field data. Various technologies, including machine learning and deep learning, predictive analytics, and computer vision, are utilized in applications such as precision farming, drone analytics, agricultural robotics, livestock monitoring, and more.North America was the largest region in the applied AI in agriculture market in 2025. The regions covered in the applied ai in agriculture market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the applied ai in agriculture market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

The applied AI in agriculture market consists of revenues earned by entities by providing services such as crop management, livestock monitoring, soil analysis, weather prediction, pest and disease detection, and supply chain optimization. The market value includes the value of related goods sold by the service provider or included within the service offering. The applied AI in agriculture market also includes sales of sensors, robots, satellite imagery systems, and field cameras. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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