Artificial Intelligence in Precision Agriculture Market Size, Share, Trends & Competitive Analysis By Type (Machine Learning, Computer Vision, Natural Language Processing, Predictive Analytics, Remote Sensing) By Application (Crop Monitoring, Yield Prediction, Soil Analysis, Irrigation Management, Pest and Disease Detection, Livestock Monitoring) By Component; By End-user; By Regions, and Industry Forecast, Global Report 2023-2030

  • Report ID: FDS335
  • Forecast Period: 2023-2030
  • No. of Pages: 150+
  • Industry: Agriculture

The global Artificial Intelligence in Precision Agriculture Market size was valued at USD 6.95 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 15.3% during the forecast period, reaching a value of USD 20.68 billion by 2030.

Artificial Intelligence in Precision 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 (AI) in Precision Agriculture refers to the integration of advanced technologies like machine learning, computer vision, and natural language processing into agricultural practices to enhance efficiency and productivity. By harnessing AI, precision agriculture aims to optimize various processes such as crop monitoring, soil analysis, irrigation management, and livestock monitoring. AI-driven solutions can process vast amounts of data from sensors, drones, satellites, and other sources to provide real-time insights, enabling farmers to make informed decisions and take proactive actions. The technology aids in predicting crop yields, detecting pests and diseases early, and efficiently allocating resources, resulting in reduced costs, minimized environmental impact, and increased crop yields for a sustainable and technologically advanced agricultural sector.

AI's role in precision agriculture has significantly transformed traditional farming practices. The technology enables the automation of tasks, allowing farmers to manage large-scale operations with greater precision and accuracy. By integrating AI-powered tools and devices, farmers can optimize resource usage, leading to reduced water and fertilizer wastage while maximizing crop output. Additionally, AI enhances data-driven decision-making by providing actionable insights based on historical and real-time data analysis. As precision agriculture continues to evolve with AI advancements, it holds the potential to address pressing challenges in the agricultural sector, including food security, resource management, and sustainable practices, ensuring a more efficient and productive future for farming and global food supply.


One of the main drivers is the growing global population and increasing food demand. As the world's population continues to rise, there is a need to produce more food with limited resources and arable land. AI-powered precision agriculture solutions offer a pathway to optimize crop yields, improve resource efficiency, and reduce waste, helping to meet the food demand sustainably. Additionally, the advancements in AI technologies, such as machine learning and computer vision, have made it possible to process and analyze massive amounts of agricultural data, enabling farmers to make data-driven decisions and implement precise farming practices for better outcomes.

However, the adoption of AI in precision agriculture also faces certain challenges and restraints. One of the primary concerns is the initial cost of implementing AI technologies and the required infrastructure. Small-scale farmers may find it financially challenging to invest in these advanced systems, hindering widespread adoption. Moreover, issues related to data privacy and security are crucial in this context, as the collection and use of sensitive agricultural data require robust measures to protect farmers' interests and prevent data misuse. Despite these challenges, the potential opportunities are substantial. AI-driven precision agriculture offers the potential to transform the entire agricultural value chain, from planting to harvesting and beyond.



Machine Learning algorithms analyze vast amounts of data from various sources, helping farmers make data-driven decisions, optimize resource usage, and predict crop yields accurately. Computer Vision enables the detection of pests, diseases, and crop health through image analysis, aiding in early intervention and targeted treatments. Natural Language Processing facilitates seamless interaction between farmers and AI-powered devices, streamlining tasks and improving overall efficiency. Predictive Analytics empowers farmers with insights into weather patterns and market trends, enabling better planning and risk management. Moreover, Remote Sensing technologies offer real-time monitoring and assessment of field conditions, allowing farmers to proactively respond to changes.


Through AI-driven Crop Monitoring, farmers can gather real-time data on crop health, growth, and environmental conditions, enabling precise interventions and resource allocation. Yield Prediction, another critical application, harnesses AI's predictive capabilities to estimate crop yields, assisting farmers in planning and decision-making for improved productivity. Soil Analysis powered by AI provides valuable insights into soil health, nutrient levels, and potential deficiencies, leading to better fertilizer management and sustainable practices. AI-driven Irrigation Management optimizes water usage, ensuring that crops receive the right amount of water at the right time, conserving water resources and enhancing crop quality. Additionally, Pest and Disease Detection using AI-enabled computer vision and machine learning aids in early identification of threats, enabling timely treatments and reducing crop losses. Lastly, Livestock Monitoring benefits from AI-based sensors and data analytics, facilitating efficient animal management and health tracking.


Hardware forms the foundational element of AI-driven precision agriculture, encompassing sensors, drones, autonomous vehicles, and other IoT devices that collect vast amounts of data from agricultural fields. These hardware components enable real-time data acquisition and play a crucial role in monitoring crops, soil conditions, and livestock. Software solutions are equally vital as they encompass the AI algorithms and data analytics platforms that process the collected data, generating valuable insights for farmers. AI-powered Software aids in crop monitoring, disease detection, yield prediction, and precision irrigation management, empowering farmers to make data-driven decisions for optimal results. Moreover, the role of Services in the AI in Precision Agriculture market is indispensable, encompassing consulting, maintenance, and support services. These services assist farmers in adopting AI technologies effectively, providing training and guidance on implementing AI solutions tailored to their specific needs.


Farmers and Growers form a prominent segment, embracing AI technologies to optimize crop management, enhance resource efficiency, and maximize yields. Agricultural Cooperatives also play a crucial role, utilizing AI-driven solutions to collectively address agricultural challenges, share resources, and collectively benefit from data-driven decision-making. Agricultural Consultants benefit from AI's advanced analytics and insights, providing valuable advice to farmers on best practices and tailored solutions. Research and Educational Institutes leverage AI for cutting-edge research, experimentation, and training future agricultural professionals in the latest technologies. Furthermore, Government Bodies are pivotal in driving AI adoption in precision agriculture through policies, incentives, and funding, aiming to boost agricultural productivity and sustainability on a broader scale.


In North America, the adoption of AI in precision agriculture is driven by the region's technologically advanced farming practices and the need to address sustainability challenges. Europe, with its focus on sustainable agriculture and environmental consciousness, embraces AI to optimize resource usage and enhance crop yields. In the Asia Pacific region, where agriculture is a crucial economic sector, AI-powered precision agriculture aids in addressing food security concerns and increasing productivity. Latin America benefits from AI's potential to improve precision farming techniques and support large-scale agricultural operations. Meanwhile, in the Middle East and Africa, AI offers valuable solutions for managing water scarcity, predicting crop yields, and mitigating climate-related risks in agriculture.


The COVID-19 pandemic has significantly impacted the Artificial Intelligence in Precision Agriculture market. As the pandemic disrupted global supply chains, restricted movement, and led to labor shortages, farmers increasingly turned to AI-powered solutions to mitigate the challenges. AI technologies proved crucial in maintaining agricultural operations, such as remote monitoring, autonomous machinery, and data analytics, enabling farmers to continue production despite the restrictions. The pandemic highlighted the importance of precision agriculture in ensuring food security and sustainability, driving further interest and investment in AI solutions. Additionally, the pandemic accelerated the digitization of the agricultural sector, promoting the adoption of AI to enhance productivity and efficiency.


Mergers & Acquisitions

  • In 2023, John Deere acquired Blue River Technology. Blue River Technology is a company that develops AI-powered agricultural solutions.
  • In 2022, CNH Industrial acquired Raven Industries. Raven Industries is a company that develops agricultural sensors and software.
  • In 2022, AGCO acquired Spraying Systems Co. Spraying Systems Co is a company that develops agricultural sprayers.

Product Launches

  • In 2023, John Deere launched its See & Spray technology. See & Spray is an AI-powered system that allows tractors to spray herbicides only where they are needed.
  • In 2022, CNH Industrial launched its Opti-N sprayer. Opti-N is an AI-powered sprayer that uses sensors to adjust the application rate of herbicides.
  • In 2022, AGCO launched its GMS system. GMS is an AI-powered system that helps farmers to manage their crops.


  • IBM Corporation
  • Microsoft Corporation
  • John Deere
  • Monsanto Company
  • Syngenta AG
  • Trimble Inc.
  • Deere & Company
  • AGCO Corporation
  • Climate Corporation
  • Descartes Labs
  • Granular Inc.
  • Prospera Technologies Ltd.
  • Taranis
  • Blue River Technology
  • PrecisionHawk
  • Farmwise
  • Gamaya
  • The Climate Corporation
  • Ceres Imaging
  • Awhere Inc.
  • Tule Technologies
  • AgEagle Aerial Systems Inc.
  • Harvest Croo Robotics
  • CNH Industrial N.V.
  • others

Table of Contents

1.1 Overview of Precision Agriculture
1.2 Role of Artificial Intelligence in Agriculture

Market Overview
2.1 Market Size and Growth Forecast
2.2 Key Players in the Market
2.3 Market Trends and Opportunities

Technology Landscape
3.1 Machine Learning Algorithms in Precision Agriculture
3.2 Computer Vision Applications
3.3 Natural Language Processing in Agriculture
3.4 Remote Sensing Technologies

Application of AI in Precision Agriculture
4.1 Crop Monitoring and Management
4.2 Yield Prediction and Optimization
4.3 Soil Analysis and Nutrient Management
4.4 Smart Irrigation Systems
4.5 Pest and Disease Detection
4.6 Livestock Monitoring and Management

Component Analysis
5.1 Hardware Components
5.2 Software Solutions
5.3 AI Services and Support

Market Segmentation by End-user
6.1 Farmers and Growers
6.2 Agricultural Cooperatives
6.3 Agricultural Consultants
6.4 Research and Educational Institutes
6.5 Government and Regulatory Bodies

Regional Analysis
7.1 North America
7.2 Europe
7.3 Asia-Pacific
7.4 Latin America
7.5 Middle East and Africa

Industry Challenges and Opportunities

Regulatory Landscape and Standards

Competitive Landscape
10.1 Key Players' Profiles
10.2 Market Share Analysis

Future Outlook and Growth Prospects


Artificial Intelligence in Precision Agriculture Market Segmentation

By Type:

  • Machine Learning
  • Computer Vision
  • Natural Language Processing
  • Predictive Analytics
  • Remote Sensing

By Application:

  • Crop Monitoring
  • Yield Prediction
  • Soil Analysis
  • Irrigation Management
  • Pest and Disease Detection
  • Livestock Monitoring

By Component:

  • Hardware
  • Software
  • Services

By End-user:

  • Farmers/Growers
  • Agricultural Cooperatives
  • Agricultural Consultants
  • Research and Educational Institutes
  • Government 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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Primary Research


Desk Research


Company Analysis


•       Identify key opinion leaders

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•       Financial Reports


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•       Mapping as per Value Chain

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Secondary Research:

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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 Precision Agriculture Market Dynamic Factors


  • Growing global population and increasing food demand
  • Need for sustainable and efficient agricultural practices
  • Advancements in AI technologies, enabling data processing and analysis
  • Rising adoption of precision agriculture for higher yields and reduced resource wastage
  • Potential to address food security challenges and enhance agricultural productivity


  • High initial investment and infrastructure costs for implementing AI solutions
  • Limited awareness and expertise in AI adoption among small-scale farmers
  • Concerns about data privacy and security in agricultural data collection and sharing


  • Integration of AI with Internet of Things (IoT) for real-time data monitoring and analytics
  • Development of AI-driven smart farming solutions for autonomous agricultural operations
  • Collaboration between tech companies and agricultural stakeholders for tailored AI solutions


  • Variability in regional and climate-specific agricultural practices requiring customized AI applications
  • Adoption barriers in traditional agricultural communities due to cultural factors and risk aversion
  • Ensuring compatibility and interoperability of AI technologies across different agricultural systems

Frequently Asked Questions

The global Artificial Intelligence in Precision Agriculture Market size was valued at USD 6.95 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 15.3% during the forecast period, reaching a value of USD 20.68 billion by 2030.

The increasing global population and food demand, the need for sustainable agricultural practices, advancements in AI technologies, rising adoption of precision agriculture, and the potential to address food security challenges.

The integration of AI with the Internet of Things (IoT) for real-time data monitoring, the development of AI-driven smart farming solutions for autonomous operations, and collaborations between tech companies and agricultural stakeholders for tailored AI solutions.

The dominance of regions or countries in the Artificial Intelligence in Precision Agriculture market can vary based on factors like technological advancements, agricultural practices, and government initiatives. North America, Europe, and Asia-Pacific are expected to be significant players in this market.

The high initial investment and infrastructure costs, limited awareness among small-scale farmers, and concerns about data privacy and security. Opportunities lie in the integration of AI with precision agriculture, the development of tailored solutions, and collaborations between stakeholders to enhance efficiency and sustainability.
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