Artificial Intelligence in Agriculture Market By Solution Type (Hardware, Software, Services), By Technology (Machine Learning, Computer Vision, Predictive Analytics, Deep Learning, Natural Language Processing (NLP), IoT Integration, and Others), By Application (Precision Farming, Livestock Monitoring, Agricultural Drones, Greenhouse Automation, Irrigation Management, Crop & Soil Health Monitoring, Weed and Pest Detection, Supply Chain Optimization, Farm Equipment Management, and Others), By Deployment (Cloud-Based, On-Premise, and Hybrid), and By End User (Agricultural Corporations, Individual Farmers, Agricultural Cooperatives, Government Agencies, Research Institutes, Agri-tech Companies, and Food Processing Units), Global Market Size, Segmental analysis, Regional Overview, Company share analysis, Leading Company Profiles And Market Forecast, 2025 – 2035

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Industry Outlook

The Artificial Intelligence in Agriculture market accounted for USD 2.25 Billion in 2024 and USD 2.81 Billion in 2025 is expected to reach USD 25.58 Billion by 2035, growing at a CAGR of around 24.73% between 2025 and 2035. Increasing food demand, precision farming, and machine learning are driving AI adoption to boost agricultural efficiency and decision-making. The AI in agricultural market is used in agritech for the better implementation of farming practices. It applies machine learning, computer vision, and IoT to support precision farming, which is one of the applications of AI, using drones, sensors, and smart cameras in the health monitoring of crops, optimization of irrigation systems, and prediction of crop yields. Other uses of AI-powered farm equipment and robotics are for improving efficiency in such tasks as pest detection, disease identification, and soil monitoring, plus avenues for livestock management and labor optimization. These AI technologies augment productivity on the farm, and they aid sustainable agriculture by reducing wastage of resources and being stewards of the environment. With the increased adherence to AI in agriculture, the software, hardware, and services are getting more niche, solving issues from smart farming to regenerative agriculture with the foresight of attaining farms that are much more efficient, sustainable, and profitable.

Industry Experts Opinion

"Adding value to raw farm products by packing, cleaning, and processing can help enhance farmers' income while reducing post-harvest losses if agriculture is integrated with the food industry through contracts, buybacks, and outsourcing of services."

• Dr. Nilabja Ghosh, Agricultural Economist

"The integration of AI in agriculture is not just about technology; it's about empowering farmers with timely information to increase yields and reduce costs."

• Dr. Mahesh Kumar, Director at AgriTech Innovations

Report Scope:

Market Dynamics

Rising global food demand increases need for agricultural efficiency.

The increasing world demand for food has swiftly brought to agriculture the application of artificial intelligence solutions. Most of these have critical implications that can be achieved using precision farming, smart farming, or digital agriculture. Artificial intelligence drives the use of resources, improves crop yield prediction, and increases the efficient functioning of farms.

Real-time information through IoT devices, drones, and sensors reduces labor mostly and helps cut down the costs associated with areas like soil health, irrigation management, and pest detection. Machines powered by robotics-based computer vision and machine learning make it possible to automate agricultural activities like disease detection in crops or monitoring animals. Keeping exact resource optimization and addressing global issues of food security make AI crucial in the sustainable practice of agriculture.

AI will significantly impact crop productivity and sustainability from the U.S. Department of Agriculture's (USDA) perspective. The AI agriculture market will grow rapidly as we see better digital tools for precision farming, but also as more precision farming-enabled, AI-powered farm equipment penetration is coupled with a growing devotion between governments and researchers in this area.

Advancements in machine learning improve predictive agricultural analytics.

The need for precision farming, resource efficiency, and improved crop production prediction accuracy is the primary driver of AI applications' explosive growth in agriculture. The world's increasing food consumption and the loss of arable land have led to the development of smart farming solutions, a broad category that encompasses AI-enhanced farm equipment, drone analytics software, IoT networking tools, and everyday necessities.

Potential synergies between machine learning, computer vision, and predictive analytics are very beneficial in soil analysis, pest monitoring, and real-time disease detection to assist farmers in making better decisions. AI irrigation and animal monitoring provide almost total sustainability, while autonomous agricultural systems and agricultural robots lessen the need for human work.

Applications for digital agriculture, like FieldView Drive 2.0, are also crucial to achieving the full potential of data-based agriculture. In conclusion, investments in AI, developments in AI hardware and software, and expanding understanding of climate-resilient farming practices are driving the push to integrate AI into precision agriculture. Increased government backing for sustainable agriculture and a rise in the use of AI-enabled crop monitoring software have also contributed to the market's expansion. These factors make AI in agriculture a key area of focus for productive, cutting-edge, and ecologically friendly farming.

Data privacy concerns restrict widespread AI technology adoption.

AI Limits the Adoption of Digital Agriculture, Precision Farming, and Smart Farming. There have been significant barriers to the adoption of AI technologies in agriculture, like digital agriculture, smart farming, and precision farming. Farmers are becoming increasingly concerned about how their data is stored and shared, and the possibility of misuse, because of the abundance of information being gathered from them through technologies such as artificial intelligence (AI) crop monitoring software, smart farm equipment, and drone analytics.

By integrating with hardware such as IoT devices, smart cameras, and other types of sensors, all these conditions increase the volume of real-time data collection from the fields on soil, livestock, and crops, raising important questions of ownership and control. Lacking clear regulations or transparent AI agriculture software policies, farmers are not fully trusting AI agriculture systems. Stagnation in the deployment of AI irrigation systems, pest detection tools, and livestock monitoring solutions is observed despite their potential for sustainable development and resource optimization due to this distrust.

Smaller farms, which possess inadequate access to secure digital infrastructure, face more challenges in safely implementing AI technologies. Moreover, the problem of the fear of cyberattacks and unauthorized access has exacerbated the challenges of adopting AI in agriculture. If strong data protection frameworks do not come up soon, growth in AI agriculture markets may remain stunted.

Integration with IoT devices enables real-time farm management.

The AI in agriculture market is growing quickly, and leading firms are driving this change with cutting-edge technologies, including computer vision, robotics, and machine learning. The agricultural applications of AI find their home in two leading companies: Benson Hill and CropIn. These organizations use AI to boost crop yield estimates and manage agricultural resource consumption. FarmWise, along with Carbon Robotics, uses artificial intelligence in their robotic systems to enhance precision weeding practices in agriculture. Blue River Technology became a part of John Deere after a significant acquisition to develop AI-based farm technologies in agricultural machinery.

As Prospera's AI platforms improve, Aerobotics' drone for crop health monitoring continues to grow, generating real-time farm condition analysis. As the demand for sustainable agriculture has grown, these companies have established AI-based industry standards. The global adoption of AI technology for precision agriculture is facilitated by startups working with large agricultural companies, which lower adoption barriers. The trend will increase since AI is still transforming farming practices and increasing output.

Expansion into developing markets creates new business possibilities.

One of the most revolutionary aspects of the AgTech sector is the variety of ways artificial intelligence (AI) may be applied to agriculture. Through the various AI-enabled solutions, including precision farming, smart cameras, and IoT devices, farmers can start managing resources, predicting crop yields, and detecting pests and diseases in their reports. Thus, these innovations advocate sustainable farming by employing predictive analytics and machine learning for irrigation management and soil monitoring.

Autonomous farming technologies, such as agricultural robotics, reduce labor and contribute to operational efficiency. AI systems also monitor the health of livestock, which enables better management of their resources. The use of AI tools, such as smart farming solutions and AI crop monitoring software, has tremendous potential for increasing production to fulfill the rising need for food, especially as the agricultural industry expands into developing nations. AI in agriculture offers a means of gaining a competitive advantage in a setting that is progressively changing due to resource-efficient methods. AI is a major factor in laying the groundwork for agriculture's exponential expansion in the future in emerging countries due to ongoing advancements.

Segment Analysis

Based on the Technology, the Artificial Intelligence in Agriculture market is classified into Machine Learning, Computer Vision, Predictive Analytics, Deep Learning, Natural Language Processing (NLP), IoT Integration, and Others. The Artificial Intelligence in Agriculture market has positioned machine learning (ML) as one of the most critical and dominating technologies, primarily enhancing crop yield prediction, irrigation optimization, and early detection of plant diseases. The algorithms in ML help to analyze extensive agricultural data that leads farmers to make decisions. These models predict trends, automate processes, and refine precision farming, and are at the heart of agricultural AI innovation. Besides, it is one core technology underpinning all the others, such as computer vision and predictive analytics, within the sector.

Based on the application, the Artificial Intelligence in Agriculture market is classified into Precision Farming, Livestock Monitoring, Agricultural Drones, Greenhouse Automation, Irrigation Management, Crop & Soil Health Monitoring, Weed and Pest Detection, Supply Chain Optimization, Farm Equipment Management, and Others. Machine learning, on the other hand, has some applications in agriculture. The major application they have in farming is precision agriculture. AI is used to make agriculture more data-driven and optimize operations concerning soil health, weather patterns, crop conditions, and so on. With this, the farmers can sometimes make real-time decisions on planting, irrigating, and fertilizing, thereby enhancing their crop yield potential and resource efficiency. Precision agriculture is indispensable for sustainable agriculture as it minimizes waste and maximizes output, characterizing precision agriculture as the key application that induces innovation and subsequent application of the sector.

Regional Analysis

The US AI in agriculture market is leading, as advanced technologies are already widely used to improve farming methods and boost output. Each region of the United States appears to be embracing AI solutions based on its agricultural focus, with the Midwest and California leading the way in crop production. The southern parts of the U.S. are concentrating more on precision livestock management. AI applications in the U.S. agriculture market are very diverse, and they include everything from drone monitoring, predictive analytics, and automated machinery to precision irrigation systems. Increased adoption of machine learning and IoT technologies has enabled farmers to make informed decisions with data that improve their crop yields, reduce costs, and enhance the efficient management of resources.

In light of climate change concerns, areas with extreme weather patterns are increasingly using artificial intelligence (AI) systems for risk assessment and weather forecasting. Venture capital investments in ag-tech companies promote innovation in areas such as autonomous agricultural equipment and AI-powered pest management. Areas with agricultural research facilities have benefited from the study of AI-supported sustainable agriculture techniques.

The two primary drivers of the Asia Pacific AI-based agricultural market's explosive growth are the expanding use of smart farming and the rising demand for food security. The leading nations in the area using AI technologies for supply chain optimization, crop monitoring, and precision farming are China, India, and Japan. Traditional farming methods are redesigned by AI technologies like drones, sensors, and production contracts to increase output, cut expenses, and lessen environmental impact. This is combined with a rise in government and private sector funding in agricultural innovation, which encourages the use of AI technologies in agriculture.

The climatic diversities and agricultural practices of the region afford suitable environments for various applications of AI across multiple crops such as rice, wheat, and fruits. Advances in machine learning and data analytics are expected to drive a more radical evolution in crop management and provide enhanced farm-to-market efficiency in future years. However, challenges such as the lack of technical infrastructure in rural areas and the lack of skilled manpower remain serious hindrances to widespread adoption.

Competitive Landscape

With the leading firms being the largest pushers and developers in this market, employing cutting-edge technologies like computer vision, robotics, and machine learning, the AI in the agriculture industry is still growing quickly and is constantly changing on its own. Benson Hill and CropIn are two of the most well-known brands in AI applications for agriculture. These businesses use artificial intelligence (AI) in the development of precision agriculture technologies to forecast and control the use of agronomic resources.

FarmWise and Carbon Robotics, manufacturers of AI-powered robotic devices, have made significant strides in precision weeding and indoor applications. After a significant acquisition, John Deere acquired Blue River Technology to help deliver AI-based farm technology for agricultural equipment. The company keeps expanding its drone for crop health monitoring, while the advancement of AI platforms under Prospera keeps providing real-time analysis of farm conditions.

The continuing and growing demand for sustainable agricultural solutions has prompted these firms to raise the bar for automated systems in contemporary farming operations. Startups working with big agricultural corporations help to smooth the path to adoption around the world for AI technologies in precision agriculture by reducing access barriers. AI is undeniably changing and will continue to change the way farming activities are conducted, with an increasing trend in its adoption.

Artificial Intelligence in Agriculture Market, Company Shares Analysis, 2024

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Recent Developments:

• In March 2025, Agmatix, an AI-powered agronomic solutions provider, announced a partnership with BASF to develop a digital tool for detecting and predicting soybean cyst nematode (SCN) infestations. The AI-driven solution aimed to help farmers with the early identification and management of SCN, a major threat to soybean yields.

Report Coverage:

By Solution Type

• Hardware
• Software
• Services

By Technology

• Machine Learning
• Computer Vision
• Predictive Analytics
• Deep Learning
• Natural Language Processing (NLP)
• IoT Integration
• Others

By Application

• Precision Farming
• Livestock Monitoring
• Agricultural Drones
• Greenhouse Automation
• Irrigation Management
• Crop & Soil Health Monitoring
• Weed and Pest Detection
• Supply Chain Optimization
• Farm Equipment Management
• Others

By Deployment

• Cloud-Based
• On-Premise
• Hybrid

By End User

• Agricultural Corporations
• Individual Farmers
• Agricultural Cooperatives
• Government Agencies
• Research Institutes
• Agri-tech Companies
• Food Processing Units

By Region

North America

• U.S.
• Canada

Europe

• U.K.
• France
• Germany
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Singapore
• Rest of Asia Pacific

Latin America

• Brazil
• Argentina
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC Countries
• South Africa
• Rest of Middle East & Africa

List of Companies:

• Benson Hill
• Bluewhite
• Carbon Robotics
• FarmWise
• CropIn
• Hippo Harvest
• Blue River Technology
• Aerobotics
• Prospera
• AgroScout
• Bonsai Robotics
• BloomX
• Harvest CROO Robotics
• Avalo
• Fieldwork Robotics

Frequently Asked Questions (FAQs)