Global AI in Agriculture Market Analysis, By Component (Hardware, Software, Services), By Technology (Machine Learning & Deep Learning, Predictive Analytics, Computer Vision), By Application (Precision Farming, Drone Analytics, Agriculture Robots, Others), and Region (North America, South America, Europe, Asia-Pacific, Middle East & Africa)—Market Size, Share, Trends & Forecast 2025-2035

AI in Agriculture Market: Overview

The global AI in Agriculture market estimated at USD 2.26 billion in 2024, and is projected to reach approximately USD 13.32 billion by 2035, at a CAGR of 20.2% from 2025 to 2035. 

The AI in agriculture market is being propelled by multiple factors, including the increasing global demand for food, the need for sustainable practices, technological advancements, government policies, and the rising adoption of precision farming. As AI continues to evolve, it is expected to revolutionize agricultural practices, making them more efficient, productive, and environmentally friendly. With AI technologies providing valuable insights, automation, and predictive capabilities, the future of agriculture is poised for a major transformation.

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Market Growth Drivers

Grosing Requirement for Sustainable Farming Practices

There is a growing global push towards sustainable agricultural practices due to concerns over climate change, environmental degradation, and the depletion of natural resources. AI can enhance sustainability in agriculture by providing real-time data on soil health, crop conditions, and water usage, helping farmers minimize the environmental impact.

AI technologies, such as drone-assisted imaging and predictive analytics, enable farmers to use precision techniques for irrigation and fertilization, ensuring that resources are used efficiently while reducing the overuse of water and chemicals.

Improved Crop Management and Yield Predictions

AI-driven solutions allow farmers to monitor their crops more efficiently by using sensors, drones, and satellite imagery. These tools provide insights into factors like soil moisture, temperature, and nutrient levels, enabling farmers to optimize planting techniques, irrigation, and fertilization schedules.

AI algorithms can also predict weather patterns, assess pest and disease risks, and forecast yields with greater accuracy. This helps farmers make proactive decisions, reduce losses, and maximize their crop productivity.

Market Restraints

Data Privacy and Security Concerns

AI in agriculture relies on the collection and analysis of vast amounts of data, such as soil conditions, crop health, and weather patterns. This data is often highly sensitive, and concerns about data privacy and security can hinder its adoption. Farmers may be wary of sharing their farm data due to fears of misuse or unauthorized access.

As agriculture becomes increasingly digitized, there is a growing risk of cyberattacks. AI-powered tools, sensors, and connected devices are vulnerable to hacking and data breaches, which could have serious consequences for farmers’ businesses and the agricultural ecosystem as a whole. 

Lack of Digital Literacy and Technical Skills

The integration of AI technologies into farming practices requires a certain level of technical knowledge and digital literacy. Many farmers, particularly in rural or developing regions, may not have the skills to operate complex AI systems or understand how to interpret data from AI-powered tools. Even when AI solutions are deployed, farmers may face challenges in understanding how to use them effectively without proper training and support. The lack of education and training programs for farmers, especially in rural areas, is a significant barrier to the adoption of AI technologies.

Market Opportunities

AI-Powered Drones and Remote Sensing

Drones equipped with AI algorithms can capture high-resolution images of crops and analyze them in real-time to monitor plant health, detect diseases, and identify pest infestations. The drones can then provide farmers with detailed reports, enabling timely interventions. Remote sensing technologies powered by AI can also map out large-scale farm operations, detecting areas that require attention, such as under-irrigated regions or nutrient-deficient soils. AI-powered drones can be used for automated crop spraying, enabling farmers to apply pesticides or fertilizers precisely where needed. This reduces the overall amount of chemicals used, lowering costs and minimizing environmental impact.

Livestock Monitoring and Management

AI-powered systems are increasingly being used in livestock farming to monitor the health and well-being of animals. Through the use of sensors and wearable devices, farmers can track vital signs, activity levels, and feeding patterns of animals. AI algorithms analyze this data to detect early signs of diseases, health issues, or abnormal behavior, enabling farmers to intervene before problems escalate. AI can predict the likelihood of disease outbreaks in livestock populations based on historical data, climate conditions, and animal behavior. This predictive capability allows farmers to take preventive measures, reducing the need for antibiotics and improving animal welfare.

AI in Agriculture Market: Report Scope

Market Analysis: By Component

Based on Component, AI in Agriculture market has been divided into Hardware, Software, Services. The software segment holds the largest share in the AI in Agriculture market. Software is critical for enabling AI-driven solutions like crop monitoring, predictive analytics, disease detection, autonomous vehicles, and yield prediction. These solutions are powered by advanced algorithms, machine learning, and deep learning technologies that help optimize farming practices.

As AI technologies become more sophisticated, there is a growing demand for software platforms that can integrate various data sources like IoT devices, sensors, drones, and satellite imagery. The continuous development of AI software and its integration into farm management systems are major drivers of this segment's growth.

Market Analysis: By Technology

Based on Technology, the AI in Agriculture market has been divided into Machine Learning & Deep Learning, Predictive Analytics, Computer Vision. Machine Learning (ML) and Deep Learning (DL) dominate the AI in Agriculture market. These technologies are at the core of many AI-driven solutions for agriculture, enabling systems to learn from data and improve decision-making processes over time. As AI systems become more advanced, ML and DL are essential for processing vast amounts of agricultural data, improving predictive models, and automating complex tasks. Their ability to continuously improve and learn from data is a major factor driving their widespread adoption.

Market Analysis: By Application

Based on Technology, the AI in Agriculture market has been divided into Precision Farming, Drone Analytics, Agriculture Robots, Others. Precision Farming holds the largest share of the AI in Agriculture market. This technology uses data-driven insights from AI to optimize farming practices, reduce waste, and improve productivity. Precision farming utilizes AI to analyze soil data, helping farmers determine the nutrient levels, moisture content, and pH balance, allowing for more targeted use of fertilizers and irrigation.

Regional Landscape

The AI in Agriculture market is segmented into North America, Europe, Asia Pacific, South America, and Middle East & Africa (MEA) based on regions. Asia Pacific is anticipated to witness the fastest CAGR from 2024 to 2030. The growth of this regional industry is attributed to its rising population, increasing agricultural productivity demands, and significant investments in agro-tech innovations by governments and other organizations.

Competitive Landscape 

The global AI in Agriculture industry is dominated by businesses like Blue River Technology (John Deere), Climate LLC, Corteva, Deere & Company, Ecorobotix SA, Farmers Edge Inc., IBM, Microsoft, Trimble Inc., VALMONT INDUSTRIES, INC. because of their unique product offerings, financial stability, strategic innovations, and regionally diverse presence The participants are focusing their efforts on promoting R&D. Additionally, they support strategic expansion activities including product launch, joint ventures, and partnerships to expand their client base and boost their market position. Some of the prominent players in the market include Blue River Technology (John Deere), Climate LLC, Corteva, Deere & Company, Ecorobotix SA, Farmers Edge Inc., IBM, Microsoft, Trimble Inc., VALMONT INDUSTRIES, INC.

Strategic Developments

• March 2024, Corteva launched Corteva Catalyst, an investment and partnership platform focused on agricultural innovations that advance the company’s R&D priorities and drive value creation. Corteva Catalyst aims to collaborate with entrepreneurs and innovators to fast-track the advancement of early-stage, groundbreaking technologies that empower farmers to produce food and feed sustainably in greater quantities.
• February 2024, Bayer and Microsoft announced the launch of the Microsoft Azure Data Manager for Agriculture platform and Bayer Ag Powered Services. This partnership aims to enhance agricultural productivity through advanced data management and analytics capabilities. The primary goal of this collaboration is to leverage cloud computing and data analytics to improve farming practices.