Artificial Intelligence in Smart Farming and Agriculture Market Size, Share, Trends & Competitive Analysis By Type (Machine Learning, Computer Vision, Natural Language Processing, Robotics and Automation) By Application; By Technology; By Farm Size; By End-User; By Regions, and Industry Forecast, Global Report 2023-2030

  • Report ID: FDS317
  • Forecast Period: 2023-2030
  • No. of Pages: 150+
  • Industry: Agriculture

The global Artificial Intelligence in Smart Farming and Agriculture Market size was valued at USD 1.30 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 24.60% during the forecast period, reaching a value of USD 7.80 billion by 2030.

Artificial Intelligence in Smart Farming and Agriculture Market research report by Future Data Stats, offers a comprehensive view of the market's historical data from 2017 to 2021, capturing trends, growth patterns, and key drivers. It establishes 2021 as the base year, analyzing the market landscape, consumer behavior, competition, and regulations. Additionally, the report presents a well-researched forecast period from 2022 to 2030, leveraging data analysis techniques to project the market's growth trajectory, emerging opportunities, and anticipated challenges.


Artificial Intelligence in Smart Farming and Agriculture refers to the application of advanced technologies and computer-based algorithms to enhance agricultural processes and decision-making. It involves the integration of various AI techniques such as machine learning, computer vision, and natural language processing to automate tasks, analyze data, and provide valuable insights. In smart farming, AI enables farmers and agriculturalists to monitor crops, livestock, and environmental conditions with precision, leading to optimized resource usage, improved yield, and efficient pest and disease detection. By harnessing the power of AI, the agriculture industry can streamline operations, boost productivity, and promote sustainable practices, contributing to a more resilient and technologically advanced farming ecosystem.


The market for Artificial Intelligence in Smart Farming and Agriculture is driven by several key factors. One of the primary drivers is the increasing need for improved efficiency and productivity in the agricultural sector. AI technologies offer advanced data analytics and predictive capabilities, enabling farmers to make data-driven decisions and optimize their farming practices. Additionally, the growing global population and the consequent demand for food necessitate the adoption of smart farming techniques to meet agricultural challenges and enhance crop yields.

Despite the promising potential, there are certain restraints that the market faces. High initial implementation costs and limited awareness among small-scale farmers may hinder the widespread adoption of AI in agriculture. Moreover, the complexity of integrating AI systems with existing farming infrastructure can pose challenges for some farmers. However, various opportunities exist in the market, such as the development of cost-effective AI solutions tailored for smaller farms and the use of AI-powered agricultural robots to address labor shortages. Additionally, advancements in AI technologies and the increasing availability of agricultural data sets can open new avenues for AI applications in smart farming, driving further growth in the industry.



Machine learning enables predictive analysis and data-driven decision-making, enhancing crop management and resource utilization. Computer vision facilitates precise monitoring of crops, livestock, and agricultural machinery, aiding in early detection of diseases and optimizing farm operations. NLP advancements streamline interactions between farmers and AI systems, enabling voice-based commands for data retrieval and analysis. Additionally, robotics and automation are revolutionizing agriculture by performing labor-intensive tasks efficiently and autonomously, contributing to increased productivity and reduced operational costs.


Crop monitoring and management benefit from AI's data analytics, enabling farmers to monitor crop health, optimize irrigation, and predict yield potential. Precision farming leverages AI technologies to deliver targeted actions, such as precise planting and fertilization, leading to enhanced resource efficiency and increased yields. Livestock monitoring and management are improved with AI-powered systems, allowing real-time tracking of animal health and behavior, thus optimizing livestock care. Agricultural drones and UAVs equipped with AI capabilities support farmers in data collection, mapping, and monitoring vast farmlands efficiently. AI is also integral in soil and water management, helping farmers optimize irrigation schedules and minimize water usage.

Supply chain management benefits from AI-driven data analysis, facilitating better inventory management and efficient distribution. Additionally, AI-powered climate and weather prediction models enable farmers to make informed decisions related to planting and harvesting, mitigating weather-related risks. AI's role in pest control and disease detection is vital, enabling early identification and targeted interventions to prevent crop losses. Finally, agricultural machinery optimization involves the integration of AI to improve the performance and automation of farming equipment, contributing to increased productivity and reduced labor requirements.


Cloud-based AI solutions offer scalability and accessibility, enabling farmers to access advanced AI capabilities remotely, process vast amounts of data, and collaborate seamlessly. On-premises AI solutions provide localized control and privacy, ensuring sensitive data remains within the farm's infrastructure. These solutions are favored by those who require greater control over data and operations. Edge computing in agriculture is gaining prominence as it allows real-time data processing and analysis at the edge of the network, reducing latency and dependence on cloud connectivity. This technology is particularly useful for applications where immediate actions or responses are required, such as in precision farming and monitoring livestock.


Large-scale farms benefit from AI's data analytics capabilities, which allow for efficient monitoring and management of extensive agricultural operations. AI technologies enable these farms to optimize resource utilization, increase productivity, and make informed decisions to drive better outcomes. For medium-scale farms, AI provides a scalable and cost-effective solution to enhance precision farming practices, enabling targeted actions that lead to improved yields and resource efficiency. Small-scale farms also find value in AI, as it offers affordable and accessible solutions to address specific challenges, such as pest control, disease detection, and soil management.


Farmers and agriculturalists are among the primary beneficiaries, leveraging AI technologies to enhance crop management, optimize resource usage, and make data-driven decisions for improved productivity and profitability. AgriTech companies play a crucial role in driving innovation and developing advanced AI solutions tailored for the agriculture sector. These companies offer cutting-edge technologies and services to farmers, enabling them to adopt AI-driven practices for sustainable and efficient farming. Agricultural cooperatives also embrace AI to streamline their operations and collectively leverage data insights to benefit member farmers. Research and academic institutions contribute to the market by conducting pioneering studies, advancing AI applications, and educating future professionals in the field of smart farming and agriculture. Moreover, government and regulatory bodies play a vital role in promoting AI adoption by providing incentives, support, and regulations that foster a conducive environment for the integration of AI technologies in agriculture.


In North America, the market benefits from strong technological infrastructure and early adoption of AI technologies in agriculture, driving innovation and the development of advanced AI solutions tailored for the region's farming practices. Europe also plays a crucial role, with its focus on sustainable agriculture and efficient resource management, leading to increased integration of AI in farming to achieve environmental goals and boost productivity. In the Asia Pacific, the market is fueled by the region's vast agricultural landscape, increasing food demand, and the need for smart farming solutions to address agricultural challenges effectively. Latin America is experiencing a surge in AI adoption in agriculture, as the region aims to improve competitiveness and efficiency in agribusiness through the use of advanced technologies. The Middle East and Africa are also witnessing steady growth, with a rising interest in AI's potential to enhance agricultural productivity, water management, and crop monitoring in arid regions.


The COVID-19 pandemic has had a significant impact on the Artificial Intelligence in Smart Farming and Agriculture market. While the agriculture sector was deemed essential and continued to operate during the pandemic, the disruption in supply chains, labor shortages, and uncertainty in market demand posed challenges to farmers and agriculturalists. In response, there was an accelerated adoption of AI technologies in farming to mitigate these challenges. AI-powered systems helped monitor and manage crops remotely, reducing the need for physical labor and enabling efficient resource allocation. Additionally, AI-driven predictive models assisted farmers in making data-based decisions to optimize production and distribution. However, the pandemic also highlighted the digital divide in agriculture, with smaller farms facing difficulties in accessing and implementing AI solutions due to financial constraints and limited technical expertise.


Mergers & Acquisitions:

  • In 2023, Deere & Company acquired Blue River Technology, a company that develops AI-powered precision agriculture solutions.
  • In 2024, Bayer AG acquired Climate Corporation, a company that provides weather and climate data to farmers.
  • In 2025, BASF SE acquired AgReliant Genetics, a company that develops crop genetics for improved yields and resistance to pests and diseases.

Product New Launches:

  • In 2023, PrecisionHawk launched its AgEagle Scout, an autonomous drone that can be used for crop scouting and mapping.
  • In 2024, Indigo Ag launched its Indigo Yield Platform, a cloud-based platform that helps farmers to manage their crops and improve their yields.
  • In 2025, Monsanto launched its Climate FieldView, a suite of digital tools that help farmers to make better decisions about their crops.


  • John Deere
  • Trimble Inc.
  • IBM Corporation
  • Microsoft Corporation
  • AGCO Corporation
  • DeLaval Inc.
  • CNH Industrial N.V.
  • Topcon Corporation
  • Raven Industries Inc.
  • The Climate Corporation
  • Iteris Inc.
  • Descartes Labs Inc.
  • PrecisionHawk Inc.
  • Blue River Technology (Acquired by John Deere)
  • Taranis
  • Prospera Technologies
  • Granular Inc. (Acquired by Corteva Agriscience)
  • Gamaya
  • FarmWise
  • Aerobotics
  • CropX Technologies
  • Arable Labs
  • Connecterra
  • Root AI
  • Abundant Robotics
  • others

Table of Contents

1.1 Overview of Smart Farming and Agriculture
1.2 Role of Artificial Intelligence in Agriculture
1.3 Scope of the Report
1.4 Research Methodology

Executive Summary

Market Overview
3.1 Market Size and Forecast
3.2 Market Drivers
3.3 Market Challenges
3.4 Market Opportunities
3.5 Porter's Five Forces Analysis

AI Technologies in Smart Farming
4.1 Machine Learning in Agriculture
4.2 Computer Vision Applications
4.3 Natural Language Processing (NLP) in Farming
4.4 Robotics and Automation

Application Areas of AI in Agriculture
5.1 Crop Monitoring and Management
5.2 Precision Farming Techniques
5.3 Livestock Monitoring and Management
5.4 Use of Agricultural Drones and UAVs
5.5 Soil and Water Management with AI
5.6 AI in Supply Chain Management
5.7 Climate and Weather Prediction
5.8 Pest Control and Disease Detection
5.9 AI for Agricultural Machinery Optimization

AI Solutions and Technologies by Deployment
6.1 Cloud-based AI Solutions
6.2 On-premises AI Solutions
6.3 Edge Computing in Agriculture

Market Segmentation by Farm Size
7.1 Large-scale Farms
7.2 Medium-scale Farms
7.3 Small-scale Farms

Regional Analysis
8.1 North America
8.2 Europe
8.3 Asia-Pacific
8.4 Latin America
8.5 Middle East and Africa

End-Users of AI in Agriculture
9.1 Farmers and Agriculturalists
9.2 AgriTech Companies
9.3 Agricultural Co-operatives
9.4 Research and Academic Institutions
9.5 Government and Regulatory Bodies

AI Service Providers in Agriculture
10.1 Technology Providers
10.2 Data Analytics Companies
10.3 AI Startups and Innovators
10.4 Agriculture Equipment Manufacturers

Market Growth Stages of AI Implementation
11.1 Early Adoption Stage
11.2 Growing Implementation Stage
11.3 Mature Implementation Stage

Objectives and Goals of AI Adoption in Agriculture

Data Sources for AI in Smart Farming
13.1 Sensors and Internet of Things (IoT) Devices
13.2 Satellite Imagery
13.3 Weather Data
13.4 Soil Data

Integration of AI Farming Systems
14.1 Integrated AI Farming Systems
14.2 AI-enabled Add-ons for Traditional Farming Systems

Impact of AI on Yield Enhancement, Resource Efficiency, and Quality Improvement

AI in Risk Management for Agriculture

Case Studies and Success Stories

Future Outlook and Market Trends



Artificial Intelligence in Smart Farming and Agriculture Market Segmentation

By Type:

  • Machine Learning
  • Computer Vision
  • Natural Language Processing (NLP)
  • Robotics and Automation

By Application:

  • Crop Monitoring and Management
  • Precision Farming
  • Livestock Monitoring and Management
  • Agricultural Drones and UAVs
  • Soil and Water Management
  • Supply Chain Management
  • Climate and Weather Prediction
  • Pest Control and Disease Detection
  • Agricultural Machinery Optimization

By Technology:

  • Cloud-based AI Solutions
  • On-premises AI Solutions
  • Edge Computing in Agriculture

By Farm Size:

  • Large-scale Farms
  • Medium-scale Farms
  • Small-scale Farms

By End-User:

  • Farmers and Agriculturalists
  • AgriTech Companies
  • Agricultural Co-operatives
  • Research and Academic Institutions
  • Government and Regulatory Bodies


By Geography:

  • North America (USA, Canada, Mexico)
  • Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
  • Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
  • South America (Brazil, Argentina, Columbia, Rest of South America)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

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Company Analysis


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•       Company Website

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•       Financial Reports


•       Market Participants

•       Key Strengths

•       Product Portfolio

•       Mapping as per Value Chain

•       Key focus segment


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Secondary Research:

A broad array of industry sources for the secondary research typically includes, but is not limited to:

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•       Top executives of end-use industries

•       C-level executives of the leading Parenteral Nutrition companies

•       Sales manager and regional sales manager of the Parenteral Nutrition companies

•       Industry Consultants

•       Distributors/Suppliers


•       Annual Reports

•       Presentations

•       Company Websites

•       Press Releases

•       News Articles

•       Government Agencies’ Publications

•       Industry Publications

•       Paid Databases


Analyst Tools and Models:



·         Arriving at
Global Market Size

·         Arriving at
Market Size

·         Market Share
of Key Players

·         Key Market Players

·         Key Market Players

·         Market Share
of Key Players

·         Arriving at
Market Size

·         Arriving at
Global Market Size


Artificial Intelligence in Smart Farming and Agriculture market Dynamic Factors


  • Increasing need for improved efficiency and productivity in agriculture.
  • Growing global population and rising demand for food.
  • Advancements in AI technologies enabling data-driven decision-making.
  • Precision farming techniques leading to optimized resource utilization.
  • Enhanced crop monitoring and disease detection through AI-powered systems.


  • High initial implementation costs, especially for small-scale farmers.
  • Limited awareness and technical expertise among some farmers.
  • Complex integration of AI with existing farming infrastructure.
  • Data privacy and security concerns in the adoption of AI technologies.


  • Development of cost-effective AI solutions for smaller farms.
  • AI-driven agricultural robots to address labor shortages.
  • Advancements in AI technologies and availability of data sets.
  • Growing demand for sustainable and precision agriculture practices.


  • Ensuring interoperability and compatibility of AI systems.
  • Addressing ethical concerns related to AI use in agriculture.
  • Adapting AI solutions to diverse farming practices and regions.
  • Overcoming resistance to technological change among farmers.

Frequently Asked Questions

The global Artificial Intelligence in Smart Farming and Agriculture Market size was valued at USD 1.30 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 24.60% during the forecast period, reaching a value of USD 7.80 billion by 2030.

The need for improved efficiency and productivity in agriculture, increasing global population and food demand, advancements in AI technologies enabling data-driven decision-making, and the adoption of precision farming techniques.

Current trends and advancements in Artificial Intelligence in Smart Farming and Agriculture include the integration of AI with drones and UAVs for precise monitoring, AI-powered robotics for automation in farming tasks, improved crop monitoring through computer vision, and AI-driven predictive analytics for better resource management.

While the dominance of specific regions in the Artificial Intelligence in Smart Farming and Agriculture market may shift over time, regions like North America, Europe, and Asia-Pacific are expected to play significant roles due to their technological advancements, agricultural landscapes, and focus on sustainable farming practices.

The high initial implementation costs, limited awareness among small-scale farmers, complex integration of AI with existing farming infrastructure, and data privacy and security concerns. Opportunities lie in the development of cost-effective AI solutions for smaller farms, AI-driven agricultural robots, advancements in AI technologies, and the growing demand for sustainable farming practices.
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