Report Description

Forecast Period


Market Size (2023)

USD 351.09 Million

Market Size (2029)

USD 705.74 Million

CAGR (2024-2029)


Fastest Growing Segment

Predictive Analytics

Largest Market

Mid-west Region

Market Overview

United States AI in Agriculture Market was valued at USD 351.09 Million in 2023 and is anticipated to project steady growth in the forecast period with a CAGR of 12.30% through 2029. Artificial Intelligence (AI) in agriculture represents the deployment of advanced technologies to improve crop yield, efficiency, and profitability. AI-driven models and tools facilitate precise farming methods, predicting weather patterns, and making sense of real-time data for better decision-making. Machine learning algorithms can identify potential diseases or pests, and automation technologies can perform mundane tasks, hence, improving overall agricultural productivity.

The AI in Agriculture market in the United States is a rapidly expanding sector, fueled by the country's strong technological infrastructure and its vast agricultural industry. It encompasses a plethora of services, software, and hardware products designed to enhance productivity, efficiency, and sustainability in farming. Market players range from tech-startups innovating in niche areas like disease identification, to major companies providing comprehensive AI-based farm management systems. Market growth is driven by factors such as increasing food demand, the need for climate-smart agriculture, and the desire to streamline farming operations. This has led to a surge in the adoption of AI technologies such as machine learning, computer vision, and predictive analytics in various agricultural practices.

Key Market Drivers

Increasing Demand for Food Security

In the dynamic landscape of agriculture, ensuring food security stands as a paramount concern. The United States AI in Agriculture market is witnessing a significant surge, fueled in part by the escalating demand for food security. Precision agriculture, empowered by AI technologies, addresses this challenge by optimizing crop yields. Through data-driven insights and predictive analytics, farmers can precisely manage resources such as water, fertilizers, and pesticides, ultimately enhancing productivity and contributing to food security objectives.

Climate change-induced uncertainties pose formidable challenges to agricultural productivity. However, AI-driven predictive analytics offer a proactive approach. By analyzing historical and real-time data, AI algorithms forecast weather patterns, pest outbreaks, and disease occurrences. This enables farmers to implement preemptive measures, safeguarding crops and ensuring consistent food production despite environmental fluctuations. The quest for food security necessitates efficient resource management. AI-powered smart farming solutions optimize resource allocation by precisely tailoring inputs to crop requirements. By integrating IoT sensors, drones, and satellite imagery, farmers gain granular insights into soil health, moisture levels, and plant conditions. This data-driven approach minimizes waste, conserves resources, and maximizes agricultural output, aligning with food security imperatives. Food security encompasses not only quantity but also accessibility. AI technologies democratize access to advanced agricultural practices, benefitting smallholder farmers. Cloud-based AI platforms and mobile applications provide affordable solutions for remote monitoring, crop management, and market access. By leveling the playing field, these technologies empower smallholder farmers to improve yields, increase incomes, and contribute to broader food security efforts.

Government initiatives play a pivotal role in fostering food security objectives and driving AI adoption in agriculture. Subsidies, grants, and incentives encourage farmers to invest in AI technologies, accelerating market growth. Additionally, regulatory frameworks that promote innovation and technology integration create an enabling environment for AI adoption. Collaborative partnerships between governments, industry stakeholders, and research institutions further amplify the impact of these initiatives, advancing food security agendas. Consumer preferences increasingly favor sustainably produced food, elevating the importance of sustainable agriculture practices. AI technologies enable precision farming techniques that minimize environmental impact while optimizing productivity. By reducing chemical inputs, conserving water, and promoting biodiversity, AI-driven sustainable agriculture aligns with consumer expectations for ethically sourced and environmentally friendly food products, thus bolstering food security efforts. The convergence of food security concerns and technological innovation has catalyzed investment in agri-tech startups. Venture capital firms and investors recognize the potential of AI-driven solutions to address global food security challenges. Consequently, funding flows into startups developing AI-powered agricultural technologies, fostering innovation and market expansion. This influx of investment capital not only propels technological advancements but also stimulates job creation and economic growth in the agricultural sector.

The increasing demand for food security serves as a powerful catalyst for the growth of the United States AI in Agriculture market. By harnessing AI technologies to optimize productivity, mitigate risks, and promote sustainable practices, stakeholders across the agricultural value chain can address food security challenges effectively. Through collaborative efforts, innovative solutions, and strategic investments, the agriculture industry can ensure a resilient and sustainable food supply for future generations.

Labor Shortages & the Desire for Automation

In the landscape of modern agriculture, labor shortages emerge as a pressing challenge, compelling stakeholders to seek innovative solutions. The United States AI in Agriculture market is witnessing notable growth, driven in part by the imperative to address labor scarcity. 

Labor shortages in agriculture necessitate the optimization of operational efficiency. AI technologies offer solutions by automating labor-intensive tasks, thereby reducing dependency on manual labor. Through machine learning algorithms and robotics, farmers can streamline processes such as planting, harvesting, and monitoring crops. This enhanced efficiency not only mitigates the impact of labor shortages but also improves productivity and profitability. Agricultural labor demand experiences significant fluctuations, particularly during peak seasons such as planting and harvest. However, labor availability often fails to align with these seasonal demands, leading to inefficiencies and operational disruptions. AI-driven automation provides a viable solution by offering continuous support throughout the agricultural cycle. By deploying autonomous machinery and smart systems, farmers can maintain consistent productivity levels despite fluctuations in labor supply, ensuring operational continuity and meeting market demands.

Escalating labor costs and wage pressures exert financial strain on agricultural operations. As labor scarcity intensifies, competition for available workers drives wages higher, eroding profit margins. AI technologies offer a cost-effective alternative by reducing labor dependency and minimizing wage-related expenses. By investing in AI-powered solutions, farmers can achieve long-term cost savings while maintaining operational efficiency and competitiveness in the market. Labor shortages underscore the need for precision agriculture solutions that optimize resource utilization and maximize productivity. AI technologies enable data-driven decision-making, allowing farmers to achieve optimal outcomes with limited labor inputs. Through real-time monitoring, predictive analytics, and autonomous equipment, farmers can optimize planting densities, irrigation schedules, and crop treatments. This precision-driven approach not only mitigates the impact of labor shortages but also enhances overall farm productivity and profitability.

Demographic shifts and changing workforce trends further exacerbate labor shortages in agriculture. As younger generations gravitate towards non-agricultural employment opportunities, the agricultural sector faces a dwindling pool of available workers. AI technologies offer a solution by bridging the gap between labor supply and demand. By automating repetitive tasks and simplifying farm operations, AI-driven solutions appeal to a diverse workforce, including tech-savvy millennials and urban migrants, thus revitalizing the agricultural labor force and ensuring its sustainability. The imperative to overcome labor shortages drives innovation and technological adoption in agriculture. AI-powered solutions represent a paradigm shift in farm management practices, offering unprecedented levels of automation and efficiency. As farmers seek to address labor challenges, they increasingly turn to AI technologies for scalable and sustainable solutions. This surge in demand stimulates innovation within the AI in Agriculture market, leading to the development of advanced tools and platforms tailored to the specific needs of farmers. Government support and incentives play a pivotal role in accelerating the adoption of AI technologies in agriculture. Recognizing the potential of AI to mitigate labor shortages and enhance productivity, governments offer subsidies, grants, and tax incentives to encourage technology adoption. Additionally, investment in research and development initiatives fosters collaboration between government agencies, academic institutions, and industry stakeholders, driving innovation and market growth in the AI in Agriculture sector.

Labor shortages represent a critical challenge facing the agricultural industry, but they also present an opportunity for innovation and growth. By embracing AI technologies, stakeholders can overcome labor constraints, optimize efficiency, and ensure the sustainability of agricultural operations. Through strategic investments, technological advancements, and collaborative partnerships, the United States AI in Agriculture market can harness the transformative potential of AI to thrive in the face of labor scarcity and emerge as a global leader in agricultural innovation.

Demand for Sustainable Agriculture Practices

Sustainable agriculture has emerged as a critical imperative in the modern agricultural landscape, driven by environmental concerns, consumer preferences, and regulatory pressures. The United States AI in Agriculture market is experiencing notable growth, largely influenced by the escalating demand for sustainable practices. Consumer preferences are shifting towards ethically sourced and environmentally friendly food products, catalyzing the adoption of sustainable agriculture practices. AI technologies enable farmers to implement precision farming techniques that minimize environmental impact while optimizing productivity. By leveraging data-driven insights and predictive analytics, farmers can reduce chemical inputs, conserve water, and promote biodiversity, aligning with consumer expectations for sustainable food production.

Regulatory frameworks and compliance standards impose stringent requirements on agricultural practices, particularly concerning environmental sustainability. AI technologies offer solutions to navigate these regulatory complexities effectively. By providing real-time monitoring, traceability, and reporting capabilities, AI-driven systems enable farmers to demonstrate compliance with environmental regulations and certification schemes. This not only mitigates regulatory risks but also enhances market access and competitiveness in an increasingly sustainability-conscious consumer landscape. Sustainable agriculture emphasizes resource efficiency and resilience, aiming to optimize resource utilization while minimizing environmental degradation. AI technologies play a pivotal role in achieving these objectives by enabling precision agriculture practices. Through advanced sensors, drones, and satellite imagery, farmers can gather granular data on soil health, moisture levels, and crop conditions. AI algorithms analyze this data to optimize irrigation schedules, fertilizer applications, and pest management strategies, resulting in resource savings and increased resilience to environmental stressors. Climate change poses significant challenges to agricultural productivity, including unpredictable weather patterns, water scarcity, and the spread of pests and diseases. Sustainable agriculture practices coupled with AI technologies offer adaptive solutions to mitigate these challenges. AI-driven predictive analytics leverage historical and real-time data to forecast climate-related risks, enabling farmers to implement proactive measures such as drought-resistant crop varieties, precision irrigation systems, and early pest detection mechanisms. By fostering climate resilience, AI in Agriculture contributes to sustainable food production and food security objectives.

Supply chain transparency and sustainability metrics are increasingly important for food retailers, processors, and consumers. AI technologies facilitate transparency and traceability throughout the agricultural supply chain, enabling stakeholders to track the origin, production methods, and environmental footprint of food products. By integrating AI-driven blockchain solutions, smart contracts, and IoT sensors, supply chain participants can verify sustainability claims, monitor compliance with ethical standards, and optimize logistics processes, ultimately enhancing trust and accountability in the food system. The demand for sustainable agriculture practices presents significant market opportunities and competitive advantages for stakeholders in the AI in Agriculture market. By aligning with sustainability trends and consumer preferences, AI-driven solutions differentiate agricultural products in the market, enabling premium pricing and market positioning. Moreover, investments in sustainable agriculture technologies foster innovation and market growth, attracting partnerships, acquisitions, and strategic alliances within the agricultural ecosystem. Collaboration and knowledge sharing are essential for advancing sustainable agriculture practices and driving innovation in the AI in Agriculture market. Industry partnerships, research consortia, and knowledge exchange platforms facilitate the transfer of best practices, technological innovations, and scientific research findings. By fostering a collaborative ecosystem, stakeholders can accelerate the adoption of sustainable agriculture technologies, address shared challenges, and unlock synergies that propel market growth and sustainability objectives.

The demand for sustainable agriculture practices represents a transformative force shaping the growth trajectory of the United States AI in Agriculture market. By harnessing AI technologies to optimize resource efficiency, mitigate climate risks, and enhance supply chain transparency, stakeholders can align with sustainability imperatives while unlocking new market opportunities and competitive advantages. Through strategic investments, collaborative partnerships, and innovative solutions, the AI in Agriculture market can emerge as a catalyst for sustainable food production and environmental stewardship, driving long-term value creation for stakeholders across the agricultural value chain.

Government Initiatives & Support for Agricultural Innovation

Government initiatives and support for agricultural innovation, including the adoption of AI, serve as significant drivers for the growth of the AI in Agriculture Market in the United States. Recognizing the immense potential benefits of AI in enhancing agricultural productivity, resource efficiency, and sustainability, governments at various levels are actively promoting and funding initiatives that encourage the seamless integration of AI technologies into farming practices. These efforts encompass a wide range of measures aimed at facilitating the widespread deployment of AI in agriculture. Financial incentives are being provided to farmers to encourage their adoption of transformative technologies, such as AI-powered smart farming systems and precision agriculture solutions. Research grants are being awarded to scientists and agricultural experts to explore and develop innovative applications of AI in various aspects of farming, from crop monitoring and yield prediction to livestock management and pest control. In addition to financial support, governments are also establishing robust policy frameworks to facilitate the integration of AI in agriculture. These frameworks encompass regulations to ensure the ethical and responsible use of AI in farming, guidelines for data privacy and security, and standards for interoperability and data sharing among different AI systems and agricultural stakeholders.

By incentivizing farmers and providing them with the necessary resources and support, these government initiatives are not only driving the growth of the AI in Agriculture Market but also paving the way for a more sustainable and efficient future in the agricultural sector. The seamless integration of AI technologies holds the promise of revolutionizing traditional farming practices, enabling farmers to make data-driven decisions, optimize resource allocation, reduce environmental impact, and enhance overall productivity and profitability. As governments continue to recognize the transformative potential of AI in agriculture, their commitment to promoting and funding these initiatives is expected to further accelerate the adoption and deployment of AI technologies in the farming industry. This, in turn, will contribute to the advancement of agricultural practices, the expansion of the AI in Agriculture Market, and the realization of a more sustainable and efficient future for the agricultural sector as a whole.

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Key Market Challenges

Data Integration & Interoperability Issues

One of the key challenges facing the United States AI in Agriculture Market is the complex nature of data integration and interoperability. Agricultural operations generate vast amounts of data from various sources, including sensors, drones, machinery, and weather stations. The challenge lies in integrating these diverse datasets into a cohesive and standardized format that AI systems can effectively analyze. Lack of interoperability between different data sources and proprietary systems hampers seamless integration, making it difficult for farmers to derive maximum value from AI applications. Overcoming this challenge requires the development of standardized data formats and protocols to ensure compatibility across different agricultural technologies.

Limited Access to High-Quality Data

While data is undeniably crucial for training AI models in agriculture, it is important to acknowledge the challenges surrounding the availability and accessibility of high-quality agricultural data. These challenges stem from various factors, such as the limited nature of agricultural datasets, their potential incompleteness, and the presence of biases that can impact the accuracy and reliability of AI algorithms. Moreover, concerns regarding data ownership, privacy, and security further complicate the landscape. Farmers may be hesitant to share their data due to apprehensions about how it will be used or safeguarded. These concerns can hinder the willingness of farmers to participate in data-sharing initiatives, which are crucial for improving the overall quality and representativeness of agricultural datasets.

In light of these challenges, collaborative efforts between stakeholders become paramount. This includes not only farmers but also technology developers and policymakers. By working together, these stakeholders can address data quality issues, establish robust data-sharing frameworks, and ensure that AI models are trained on diverse and representative datasets. By delving deeper into these considerations, we can better understand the complex interplay between data, AI, and agriculture, and pave the way for more effective and responsible use of AI technologies in the field.

Key Market Trends

Precision Agriculture Adoption is on the Rise

A significant trend in the United States AI in Agriculture Market is the increasing adoption of precision agriculture practices. Precision agriculture involves the use of AI, sensors, and other cutting-edge technologies to optimize farming processes at the field level. By leveraging AI applications, data from various sources, including satellite imagery, sensors, weather forecasts, and historical data, is meticulously analyzed to provide farmers with detailed insights into crop health, soil conditions, and resource utilization. This in-depth analysis empowers farmers to make informed decisions in resource management, allowing them to implement precise irrigation techniques, targeted fertilization strategies, and optimal pest control measures. These advancements not only enhance productivity and reduce environmental impact but also contribute to the overall sustainability of farming practices. The integration of AI in agriculture is revolutionizing the way farmers approach challenges and equips them with advanced tools to achieve long-term sustainable farming practices.

Adoption of Data-Driven Decision-Making in Farming Operations

The trend of data-driven decision-making is rapidly gaining momentum in the United States AI in Agriculture Market. Farmers are increasingly embracing AI technologies to collect, analyze, and interpret vast amounts of data, enabling them to make well-informed decisions that optimize their agricultural practices. With the help of AI, farmers can now accurately predict optimal planting times, efficiently detect and combat disease outbreaks, and ensure precise resource management. The integration of AI-driven analytics platforms empowers farmers to receive actionable insights in real-time, leading to improved operational efficiency and enhanced sustainability. This transformative trend signifies a paradigm shift from traditional, experience-based decision-making to more data-centric and evidence-based practices in the field of agriculture.

Segmental Insights

Technology Insights

Based on the Technology, in the United States, the AI in Agriculture market is experiencing a significant rise in the application of Predictive Analytics. This cutting-edge technology is primarily being used to analyze complex patterns and trends from vast datasets, empowering farmers to make highly accurate forecasting decisions regarding crop yield and potential pest infestations. By leveraging the power of Machine Learning and Computer Vision, farmers can optimize various facets of agricultural operations, including irrigation management, soil health assessment, and equipment maintenance. However, the current dominance of Predictive Analytics in the agricultural landscape can be attributed to its direct and tangible impact on improving productivity, reducing farm-related risks, and ensuring sustainable farming practices. By harnessing the potential of AI, farmers are embracing a data-driven approach that not only enhances efficiency but also promotes environmental stewardship and food security for a rapidly growing population.

Offering Insights

Based on the Offering, in the United States, the AI in Agriculture market is currently undergoing a remarkable transformation. The emergence of AI-As-A-Service as the dominant solution is surpassing traditional Hardware and Software options. This shift is primarily driven by the remarkable benefits offered by AI-As-A-Service models, which include unparalleled scalability, flexibility, and cost-effectiveness. One of the most significant advantages of AI-As-A-Service is its ability to enable even small-scale farmers to leverage the power of AI. Previously, high upfront costs posed a significant barrier for these farmers to adopt AI technologies. However, with AI-As-A-Service, they can now gain access to advanced capabilities without the burden of hefty initial investments.

This democratization of AI in agriculture is fueling an unprecedented acceleration in the adoption of AI technologies within the sector. As a result, the industry's landscape is being revolutionized, paving the way for a more prosperous and technologically driven future. Also, the integration of AI technologies in agriculture is expected to bring about significant advancements in efficiency, productivity, and sustainability. From precision farming techniques to automated data analysis, AI has the potential to optimize various aspects of agricultural practices. This, in turn, can contribute to increased crop yields, reduced resource wastage, and enhanced environmental stewardship. Overall, the AI revolution in the agricultural sector holds immense promise for transforming the way we produce food and manage our natural resources. With the continued advancements in AI technologies and the wider accessibility provided by AI-As-A-Service models, a more efficient, productive, and sustainable future awaits the industry.

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Regional Insights

The Midwestern region of the United States, often referred to as the "breadbasket" of America, is currently leading the charge in the AI in Agriculture Market. This region, known for its vast network of farms and agricultural enterprises, is wholeheartedly embracing AI technologies to transform the industry. By leveraging innovative solutions, these forward-thinking farmers are optimizing crop yield, streamlining operations, and even harnessing AI's predictive capabilities to accurately forecast weather patterns. The integration of AI in agriculture is revolutionizing the sector, enhancing productivity, and ensuring long-term sustainability. With AI-powered insights, farmers can make data-driven decisions, optimize resource allocation, and minimize waste. This not only leads to higher crop yields but also reduces the environmental impact of farming practices.

Likewise, the Midwestern region's commitment to AI in agriculture is paving the way for a more efficient and resilient future. By embracing cutting-edge technologies, farmers can adapt to changing market demands, mitigate risks, and enhance overall profitability. From automated irrigation systems to precision agriculture techniques, AI is driving innovation and transforming traditional farming practices. The Midwestern region's adoption of AI in agriculture is not only revolutionizing the industry but also setting a precedent for other regions to follow. By harnessing the power of AI, farmers are creating a more sustainable, efficient, and resilient agricultural landscape that will benefit both present and future generations.

Recent Developments

  • In March 2024, UCF Researchers are spearheading a project aimed at developing AI-driven technologies tailored for agriculture. The study will focus on enhancing field operations within the industry, facilitating precise and well-informed decision-making processes. AI, beyond its conventional applications in electronics and autonomous vehicles, is proving instrumental in revolutionizing agriculture. Through innovations such as soil and water monitoring sensors and autonomous crop-harvesting robots, AI is driving efficiency, sustainability, and cost-effectiveness in farming operations.

Key Market Players

  • International Business Machines Corporation (IBM)
  • Granular, Inc.
  • Microsoft 
  • Deere & Company
  • Awhere Inc.
  • Climate LLC.
  • Agribotix, LLC
  • Descartes Labs Inc.
  • Valmont Industries, Inc.

By Technology

By Offering

By Application

By Region

  • Machine Learning
  • Predictive Analytics
  • Computer Vision
  • Hardware
  • Software
  • AI-As-A-Service
  • Precision Farming
  • Livestock Monitoring
  • Agriculture Robots
  • Drone
  • Others
  • North-east
  • Mid-west
  • West
  • South

Report Scope:

In this report, the United States AI in Agriculture Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

  • United States AI in Agriculture Market, By Technology:

o   Machine Learning

o   Predictive Analytics

o   Computer Vision

  • United States AI in Agriculture Market, By Offering:

o   Hardware

o   Software

o   AI-As-A-Service

  • United States AI in Agriculture Market, By Application:

o   Precision Farming

o   Livestock Monitoring

o   Agriculture Robots

o   Drone

o   Others

  • United States AI in Agriculture Market, By Region:

o   North-east 

o   Mid-west 

o   West 

o   South 

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the United States AI in Agriculture Market.

Available Customizations:

United States AI in Agriculture Market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

United States AI in Agriculture Market is an upcoming report to be released soon. If you wish an early delivery of this report or want to confirm the date of release, please contact us at [email protected]

Table of content

1.    Product Overview

1.1.  Market Definition

1.2.  Scope of the Market

1.2.1.          Markets Covered

1.2.2.          Years Considered for Study

1.2.3.          Key Market Segmentations

2.    Research Methodology

2.1.  Objective of the Study

2.2.  Baseline Methodology

2.3.  Key Industry Partners

2.4.  Major Association and Secondary Sources

2.5.  Forecasting Methodology

2.6.  Data Triangulation & Validation

2.7.  Assumptions and Limitations

3.    Executive Summary

3.1.  Overview of the Market

3.2.  Overview of Key Market Segmentations

3.3.  Overview of Key Market Players

3.4.  Overview of Key Regions/Countries

3.5.  Overview of Market Drivers, Challenges, Trends

4.    Voice of Customer

5.    United States AI in Agriculture Market Outlook

5.1.  Market Size & Forecast

5.1.1.          By Value

5.2.  Market Share & Forecast

      5.2.1.    By Technology (Machine Learning, Predictive Analytics and Computer Vision)

      5.2.2.    By Offering (Hardware, Software and AI-As-A-Service)

      5.2.3.    By Application (Precision Farming, Livestock Monitoring, Agriculture Robots, Drone and Others)

      5.2.4.    By Region

      5.2.5.    By Company (2023)

5.3.  Market Map

6.    North-East AI in Agriculture Market Outlook

6.1.  Market Size & Forecast       

6.1.1.          By Value

6.2.  Market Share & Forecast

6.2.1.          By Technology

6.2.2.          By Offering

6.2.3.          By Application

7.    Mid-West AI in Agriculture Market Outlook

7.1.  Market Size & Forecast       

7.1.1.          By Value

7.2.  Market Share & Forecast

7.2.1.          By Technology

7.2.2.          By Offering

7.2.3.          By Application

8.    West AI in Agriculture Market Outlook

8.1.  Market Size & Forecast       

8.1.1.          By Value

8.2.  Market Share & Forecast

8.2.1.          By Technology

8.2.2.          By Offering

8.2.3.          By Application

9.    South AI in Agriculture Market Outlook

9.1.  Market Size & Forecast       

9.1.1.          By Value

9.2.  Market Share & Forecast

9.2.1.          By Technology

9.2.2.          By Offering

9.2.3.          By Application

10. Market Dynamics

10.1.             Drivers

10.2.             Challenges

11. Market Trends & Developments

11.1.             Recent Development

11.2.             Mergers & Acquisitions

11.3.             Product Launches

12. Policy & Regulatory Landscape

13. United States Economic Profile

14. United States AI in Agriculture Market: SWOT Analysis

15. Porter’s Five Forces Analysis

15.1.             Competition in the Industry

15.2.             Potential of New Entrants

15.3.             Power of Suppliers

15.4.             Power of Customers

15.5.             Threat of Substitute Products

16. Competitive Landscape

16.1.   International Business Machines Corporation (IBM)

16.1.1.        Business Overview

16.1.2.        Product Offerings

16.1.3.        Recent Developments

16.1.4.        Financials (As Reported)

16.1.5.        Key Personnel

16.2.   Granular, Inc.

16.3.   Microsoft 

16.4.   Deere & Company

16.5.   Awhere Inc.

16.6.   Climate LLC

16.7.   Agribotix, LLC

16.8.   Descartes Labs Inc.

16.9. Valmont Industries, Inc.

17.  Strategic Recommendations

18.  About Us & Disclaimer

Figures and Tables

Frequently asked questions


The market size of the United States AI in Agriculture Market was estimated to be USD 351.09 Million in 2023.


International Business Machines Corporation (IBM), Granular, Inc., Microsoft, Deere & Company are the top key players in the United States AI in agriculture Market in 2023.


Data Integration & Interoperability Issues and Limited Access to High-Quality Data are the major challenges which can restrict the growth of the United States AI in agriculture Market.


Increasing demand for food security and Labor Shortages & the Desire for Automation will propel United States AI in Agriculture market growth during the forecast period.


Sakshi Bajaal

Business Consultant
Press Release

United States AI in Agriculture Market to Grow with a CAGR of 12.30% through 2029

Apr, 2024

Increasing need for precision agriculture and adoption of data-driven decision-making in farming operations is expected to drive the United States AI in Agriculture Market growth in the forecast peri