# US Applied AI in Agriculture Market > US Applied AI in Agriculture Market Research Report: By Technology (Machine Learning, Computer Vision, Predictive Analytics), By Offering (Software, AI-as-a-Service) and By Application (Drone Analytics, Precision Farming) - Forecast to 2035 - **Forecast Period:** 2025 - 2035 - **CAGR:** 14.18% - **2024:** $ 655.61 Million - **2025:** $ 748.58 Million - **2035:** $ 2,820.47 Million - **Key Players:** Bayer (DE), Corteva Agriscience (US), Syngenta (CH), John Deere (US), AG Leader Technology (US), Trimble (US), Taranis (IL), Farmers Edge (CA), Aker Solutions (NO) **Report ID:** MRFR/ICT/13470-HCR · **Pages:** 100 · **Author:** Apoorva Priyadarshi & Garvit Vyas · **Last Updated:** April 06, 2026 **URL:** https://www.marketresearchfuture.com/reports/us-applied-ai-in-agriculture-market-14997 --- ## Market Summary ## **US Applied AI in Agriculture Market Overview** As per MRFR analysis, the US Applied AI in Agriculture Market Size was estimated at 407.4 (USD Million) in 2023. The US Applied AI in Agriculture Market Industry is expected to grow from 600(USD Million) in 2024 to 10,000 (USD Million) by 2035. The US Applied AI in Agriculture Market CAGR (growth rate) is expected to be around 29.145% during the forecast period (2025 - 2035). ## **Key US Applied AI in Agriculture Market Trends Highlighted** The US applied AI in agriculture market is experiencing notable trends driven by heightened attention towards sustainable farming practices. Farmers are increasingly adopting technologies that utilize AI to optimize crop yields while minimizing resource usage, promoting environmental sustainability. This shift is largely influenced by government initiatives focused on agricultural innovation, indicating a supportive regulatory framework that encourages technological advancements in the sector. Precision agriculture, powered by AI, continues to gain traction, enabling farmers to analyze data from various sources such as drones, satellites, and sensors to make informed decisions about planting and resource allocation. There are significant opportunities in the area of data analytics, as the vast amounts of data generated by farms can be harnessed to improve decision-making processes. Startups focusing on AI applications for predictive analysis of crop health or disease detection are gaining investments and support. The integration of AI with IoT devices also presents a chance for growth, as these technologies can work together to create a more connected agricultural ecosystem, which is essential for enhancing productivity. Recent trends show increasing collaboration between technology companies and agricultural organizations. This partnership fosters innovation and accelerates the development of AI solutions tailored to the specific needs of US farmers. Moreover, consumer demand for transparency and sustainability is prompting farmers to leverage AI to provide traceability and improve food safety protocols. This shifting landscape in the US agriculture sector is setting the foundation for a more efficient and sustainable future through applied AI technologies. Source: Primary Research, Secondary Research, _Market Research Future_ Database and Analyst Review ## **US Applied AI in Agriculture Market Drivers** ### **Rising Demand for Food Production** The growing population in the United States is driving significant demand for efficient food production methods. According to the U.S. Department of Agriculture, the U.S. population is expected to reach over 350 million by 2030, creating a pressing need for enhanced agricultural productivity. The U.S Applied AI in Agriculture Market Industry is well positioned to address this need through automation and data-driven insights. Major players like Cargill Incorporated and Monsanto are investing heavily in AI-driven technologies to optimize crop yields and improve food quality. Moreover, with the USDA projecting the need to increase food production by approximately 70% to meet the needs of 2050, the application of AI technologies is crucial for maximizing efficiency and minimizing resource consumption in agriculture. ### **Technological Advancements in Agricultural Machinery** The integration of advanced technologies such as machine learning, computer vision, and drone technology has revolutionized agricultural practices in the U.S. A report by the American Farm Bureau Federation highlighted that nearly 80% of farmers in the U.S. are employing technology to enhance their operations. The U.S Applied AI in Agriculture Market Industry is benefiting from this trend as companies like John Deere and AG Leader Technology continue to innovate. These organizations are incorporating AI into their machinery to facilitate precision farming, automate irrigation systems, and provide real-time analytics which improves decision-making, thereby significantly optimizing yield and reducing inefficiencies. ### **Government Grants and Support for Agricultural Innovation** The U.S. government has taken significant steps to promote innovation in agriculture, particularly through funding initiatives and grants targeting technology adoption. Federal programs such as the USDA’s Agricultural Innovation Agenda aim to invest in sustainable agriculture practices and support technology advancements. With billions allocated toward Research and Development in agricultural technology, the U.S Applied AI in Agriculture Market Industry stands to gain enormously. Recent statistics indicate that investment in agricultural technology could reach $10 billion annually, largely driven by government incentives aimed at fostering advancements that enhance food security and sustainability. This governmental support encourages private sector participation and expansion in the AI segment. ## **US Applied AI in Agriculture Market Segment Insights** ### **Applied AI in Agriculture Market Technology Insights** The Technology segment of the US Applied AI in Agriculture Market is vital for enhancing agricultural efficiency and productivity through innovative solutions. Among its prominent components, Machine Learning plays a critical role by enabling precision agriculture techniques that assist in analyzing vast amounts of data, thus optimizing crop yields and resource utilization. This technology allows farmers to make data-driven decisions, ultimately leading to increased food production and sustainability in a growing population. Computer Vision serves as another essential aspect, providing capabilities to monitor crop health, detect diseases, and assess soil conditions through image analysis and real-time monitoring. This technology facilitates quicker and more accurate assessments compared to traditional methods, helping farmers to identify problems sooner and act accordingly. Predictive Analytics further enriches the landscape by helping stakeholders foresee potential risks and trends based on historical data and current conditions. It enables proactive management and planning, allowing farmers to adjust their strategies for production and resource management based on anticipated market demands or weather changes. Collectively, these technologies are positioned to disrupt traditional farming practices in the US, driving revenue growth and innovation within the industry. The integration of these advancements promises not only improved operational efficiency but also enhanced sustainability practices, contributing to the overall goals of achieving food security while minimizing environmental impacts. As consumers continue to demand transparency and sustainability in food production, the US Applied AI in Agriculture Market is set to evolve rapidly, influenced significantly by advancements in these key technological areas. The demand for innovative solutions such as these is being fueled by various market drivers including the increasing need for food security, advancements in data collection and processing technologies, and the growing awareness of sustainable agricultural practices. However, challenges such as the need for significant capital investment and data privacy concerns could impact the adoption rates of these technologies. In this dynamic environment, the US Applied AI in Agriculture Market presents both opportunities and hurdles as it continues to expand and mature, driven largely by advancements in Technology and its related segments. Source: Primary Research, Secondary Research, _Market Research Future_ Database and Analyst Review ### **Applied AI in Agriculture Market Offering Insights** The Offering segment of the US Applied AI in Agriculture Market plays a critical role in driving innovation and efficiency within the sector. This segment primarily encompasses Software and AI-as-a-Service, both of which are increasingly crucial for modern agricultural practices. Software solutions enable farmers to utilize data analytics, enabling smarter decisions regarding crop management, pest control, and yield forecasting. Meanwhile, AI-as-a-Service provides scalable algorithms and machine learning tools that allow farmers to integrate AI capabilities without the need for substantial upfront investment. This adaptability is particularly important for a diverse landscape of farmers and agricultural businesses across the US, addressing different levels of technological capability and financial resources. Collectively, these offerings are significant contributors to the optimization of resources, enhancement of productivity, and overall sustainability of agricultural practices, indicating a transformative shift in how agriculture operates through the incorporation of technology in everyday practices. With the agricultural sector in the US striving for improved profitability and sustainability, the integration of advanced AI solutions is poised to play a pivotal role in addressing the challenges faced by the industry. ### **Applied AI in Agriculture Market Application Insights** The Application segment of the US Applied AI in Agriculture Market is rapidly evolving and gaining attention for its transformative impact on farming practices. Technologies like Drone Analytics and Precision Farming play a crucial role in enhancing agricultural productivity and sustainability. Drone Analytics offers farmers the ability to gather real-time data on crop health, soil conditions, and overall farm management, enabling more informed decision-making. This segment not only improves efficiency but also helps in precision application of resources, leading to reduced waste and increased yield. Precision Farming further complements this trend by employing data-driven techniques to optimize field-level management regarding crop farming. With the increasing demand for food safety and quality, these applications are becoming essential, as they aid in monitoring pest and disease outbreaks effectively. As farmers look for ways to implement smart farming practices, applying AI in agriculture provides them with valuable insights to streamline operations for better economic returns. The growing interest in sustainable agriculture and the commitment to enhancing food security in the US further amplify the significance of this segment in the industry. Overall, the Application segment is poised for substantial growth, driven by technological advancements and a focus on efficiency and sustainability. ## **US Applied AI in Agriculture Market Key Players and Competitive Insights** The US Applied AI in Agriculture Market has experienced rapid advancements driven by the integration of artificial intelligence technologies that enhance farming practices, crop management, and resource utilization. This sector has become increasingly competitive as various players seek to leverage AI-driven solutions that provide more efficient farming methods and real-time data analysis. Innovations in areas such as machine learning, data analytics, and autonomous machinery are reshaping traditional agriculture, encouraging companies to develop solutions that optimize productivity while minimizing environmental impact. As farmers face challenges such as climate change, labor shortages, and the need for sustainable practices, applied AI is emerging as a critical tool that can help address these issues. The competitive landscape is marked by collaborations between tech companies and agriculture firms, signaling a trend toward integrated solutions aimed at revolutionizing the agricultural industry.FarmWise has positioned itself as a significant player within the US Applied AI in Agriculture Market by focusing on the development and deployment of sophisticated AI-driven machinery designed for weeding and crop care. The company specializes in creating autonomous robots equipped with advanced vision systems that can identify weeds and distinguish them from crops, enabling precision agriculture practices that reduce herbicide usage and increase crop yields. FarmWise's strength lies in its innovative technologies that enhance operational efficiency and sustainability. The company's presence in the market is bolstered by strong partnerships with growers and agricultural organizations seeking to improve their production processes. This strategic focus on automation and sustainability allows FarmWise to maintain a competitive edge and respond effectively to the evolving needs of the agriculture sector. John Deere, a well-established entity in the agricultural machinery industry, has made significant strides in the US Applied AI in Agriculture Market through the incorporation of AI technology into its product offerings. The company delivers a range of advanced solutions, including autonomous tractors, precision agriculture tools, and data analytics platforms that enable farmers to make informed decisions about planting, irrigation, and harvesting. John Deere's strengths are evident in its extensive distribution network and strong brand reputation built over decades. The company has actively engaged in mergers and acquisitions to enhance its technology portfolio and strengthen its market presence, allowing it to integrate AI capabilities into traditional equipment. By continuously innovating and investing in research and development, John Deere is well-positioned to lead the charge in adopting AI technologies for agriculture, providing essential tools that help farmers address contemporary agricultural challenges. ### **Key Companies in the US Applied AI in Agriculture Market Include** ### **US Applied AI in Agriculture Market Industry Developments** Recent developments in the US Applied AI in Agriculture Market highlight significant advancements and increasing investments in technologies aimed at enhancing agricultural productivity. Companies such as FarmWise, John Deere, and PrecisionHawk are leading the charge with innovative solutions that utilize AI for precision farming, pest detection, and crop management. Current affairs reflect a growing trend towards sustainability, with firms like BASF and Bayer leveraging AI to improve yield while minimizing environmental impact. In recent months, Trimble announced a merger with Raven Industries in April 2023, enhancing its technology portfolio for agricultural productivity, with widespread media coverage confirming the move. Additionally, IBM's partnership with various agricultural firms aims to integrate AI into supply chain solutions, improving logistics and forecasting. Over the past two years, significant milestones have included Corteva's launch of AI-driven crop simulation tools in July 2022 and Ag Leader Technology increasing its market share through strategic enhancements in data analytics capabilities. Overall, the valuation of AI in agriculture is expected to grow as technology adoption accelerates, driving efficiency and sustainability in farming practices across the United States. ### **US Applied AI in Agriculture Market Segmentation Insights** #### **Applied AI in Agriculture Market Technology****Outlook** #### **Applied AI in Agriculture Market Offering Outlook** #### **Applied AI in Agriculture Market Application Outlook** ## Market Drivers ### Government Support and Funding Government initiatives and funding programs play a crucial role in propelling the applied ai-in-agriculture market. Various federal and state agencies are investing in research and development to promote the integration of AI technologies in farming practices. For example, the USDA has allocated millions of dollars to support projects that leverage AI for improving crop management and pest control. Such financial backing not only encourages innovation but also facilitates the adoption of AI solutions among farmers. The increasing availability of grants and subsidies is likely to enhance the market landscape, as more agricultural stakeholders recognize the potential benefits of AI. This supportive environment is expected to drive growth in the applied ai-in-agriculture market, fostering advancements that align with national agricultural goals. ### Rising Demand for Food Security The growing global population and the corresponding demand for food security are significant drivers of the applied ai-in-agriculture market. As the U.S. population is projected to reach over 350 million by 2030, the pressure on agricultural systems to produce more food sustainably intensifies. AI technologies offer solutions to enhance crop productivity and resource management, addressing the challenges posed by climate change and limited arable land. The applied ai-in-agriculture market is responding to this demand by providing tools that enable farmers to make data-driven decisions, ultimately leading to increased food production. This trend is likely to continue, as stakeholders in the agricultural sector seek innovative methods to ensure food availability and quality in the face of rising demand. ### Technological Advancements in AI The rapid evolution of artificial intelligence technologies is a primary driver for the applied ai-in-agriculture market. Innovations in machine learning, computer vision, and data analytics are enabling farmers to optimize crop yields and reduce resource wastage. For instance, AI algorithms can analyze vast datasets from sensors and drones, providing actionable insights for precision farming. The market for AI in agriculture is projected to reach approximately $2.6 billion by 2027, reflecting a compound annual growth rate (CAGR) of around 25%. This growth is indicative of the increasing reliance on advanced technologies to enhance agricultural productivity and sustainability. As farmers adopt these technologies, The applied AI in agriculture market will expand significantly, driven by the need for efficiency and profitability. ### Integration of IoT in Agriculture The integration of Internet of Things (IoT) technologies with AI is emerging as a key driver for the applied ai-in-agriculture market. IoT devices, such as sensors and smart equipment, collect real-time data on soil conditions, weather patterns, and crop health. When combined with AI analytics, this data can provide farmers with insights that enhance decision-making processes. The synergy between IoT and AI is expected to revolutionize farming practices, leading to increased efficiency and productivity. As the number of connected devices in agriculture continues to grow, the applied ai-in-agriculture market is likely to expand, driven by the demand for smarter farming solutions that leverage data for improved outcomes. ### Consumer Preference for Sustainable Practices There is a noticeable shift in consumer preferences towards sustainably produced food, which is influencing the applied ai-in-agriculture market. As consumers become more environmentally conscious, they are increasingly seeking products that are grown using sustainable practices. This trend is prompting farmers to adopt AI technologies that facilitate sustainable farming methods, such as precision irrigation and integrated pest management. By utilizing AI, farmers can minimize chemical usage and optimize resource allocation, aligning their practices with consumer expectations. The applied ai-in-agriculture market is likely to benefit from this shift, as more agricultural producers invest in AI solutions to meet the demand for sustainable food options, thereby enhancing their market competitiveness. ## Future Outlook The [Applied AI in Agriculture Market](https://www.marketresearchfuture.com/reports/applied-ai-in-agriculture-market-12168) is projected to grow at a 14.18% CAGR from 2025 to 2035, driven by advancements in precision farming, data analytics, and automation technologies. **New opportunities:** - Development of AI-driven pest management solutions - Integration of IoT with AI for real-time crop monitoring - Creation of predictive analytics platforms for yield forecasting By 2035, the market is expected to achieve substantial growth, driven by innovative technologies and strategic investments. ## Segment Insights ### By Technology: Machine Learning (Largest) vs. Computer Vision (Fastest-Growing) In the US applied ai-in-agriculture market, Machine Learning holds the largest share, significantly outpacing its counterparts. Its widespread implementation across various agricultural sectors underscores its critical role in enhancing operational efficiencies and yield predictions. Computer Vision, while smaller in comparison, is rapidly gaining traction, driven by advancements in imaging technologies and the increasing need for precision agriculture solutions. The growth trends in this segment indicate a vibrant landscape where Predictive Analytics, although an emerging player, is witnessing substantial interest from stakeholders looking for data-driven decision-making tools. The ongoing digitization of agriculture is a key growth driver, as farmers increasingly rely on technology to optimize productivity and resource management. Machine Learning continues to attract investments due to its proven benefits, while Computer Vision is reshaping the way visual data is processed and utilized in the sector. Technology: Machine Learning (Dominant) vs. Computer Vision (Emerging) Machine Learning stands out as the dominant technology within this segment, powered by its ability to analyze vast datasets to provide actionable insights for crop management and yield forecasting. Its integration into farm machinery and decision support systems enhances efficiency, making it essential for modern farming. On the other hand, Computer Vision is an emerging technology that leverages image recognition and analysis to monitor crop health and detect diseases early. This technology is particularly valuable in precision agriculture, where farmers seek to maximize yields and minimize resource waste. As the agricultural landscape evolves, both technologies will likely coexist and complement each other, driving innovation in farming practices. ### By Offering: Software (Largest) vs. AI-as-a-Service (Fastest-Growing) In the US applied ai-in-agriculture market, the Software segment commands the largest share, reflecting its established presence and integration into farming practices. This segment enjoys widespread adoption due to its versatility and capability in enhancing various agricultural processes such as crop management, yield prediction, and resource optimization. On the other hand, AI-as-a-Service is emerging as a fast-growing segment, driven by the increasing demand for scalable and flexible AI solutions that minimize upfront investment and operational overhead costs. The growth trends in the US applied ai-in-agriculture market showcase a shift towards cloud-based AI solutions, with AI-as-a-Service gaining traction in recent years. Key drivers include the need for cost-efficient agricultural technologies and the rising adoption of automation in farming practices. As farmers seek to leverage data and analytics for improved decision-making, the demand for both Software and AI-as-a-Service offerings is expected to continue to rise, catering to a wide range of agricultural needs. Software (Dominant) vs. AI-as-a-Service (Emerging) The Software segment is characterized by its robust capabilities in delivering comprehensive agricultural solutions, including farm management software, precision agriculture tools, and data analytics applications. This dominance arises from its ability to integrate easily with existing farming systems and provide actionable insights that enhance productivity. In contrast, AI-as-a-Service is viewed as an emerging solution that offers flexibility and access to powerful AI tools without significant upfront costs. This segment appeals particularly to smaller farms and agritech startups looking to harness AI technology without substantial investment in infrastructure. As these offerings evolve, both segments are likely to play pivotal roles in transforming agricultural practices. ### By Application: Drone Analytics (Largest) vs. Precision Farming (Fastest-Growing) Within the application segment of the US applied ai-in-agriculture market, Drone Analytics holds a significant market share, leading as the largest application type due to its widespread adoption for monitoring crop health and optimizing farming practices. Conversely, Precision Farming is rapidly gaining traction, becoming the fastest-growing segment as farmers embrace technologies that enhance crop yields through data-driven decision-making and resource management. The growth trends in this segment reflect the increasing need for efficiency and sustainability in agricultural practices. Drone Analytics is becoming indispensable for real-time data collection and analysis, while Precision Farming's rise is fueled by advancements in IoT and AI technologies. Both applications symbolize a transformative shift in agriculture, as stakeholders seek to leverage innovative solutions for improved productivity and reduced environmental impact. Application: Drone Analytics (Dominant) vs. Precision Farming (Emerging) Drone Analytics is currently the dominant force in the application segment, characterized by a robust capability to deliver high-resolution imagery and actionable insights for precision agriculture. This technology is pivotal in facilitating timely interventions, thereby enhancing crop management and sustainability. On the other hand, Precision Farming, as an emerging application, focuses on using data analytics and AI to optimize field-level management regarding crop farming. This method is becoming increasingly favored due to its ability to increase efficiency and minimize waste. The convergence of these two applications is driving innovation in the industry, fostering a new era of smart agriculture that enhances productivity while addressing environmental challenges. ## Competitive Benchmarking The applied ai-in-agriculture market is currently characterized by a dynamic competitive landscape, driven by technological advancements and increasing demand for sustainable agricultural practices. Key players such as Bayer (DE), Corteva Agriscience (US), and John Deere (US) are at the forefront, each adopting distinct strategies to enhance their market positioning. Bayer (DE) focuses on innovation through its digital farming solutions, aiming to integrate AI with traditional agricultural practices. Corteva Agriscience (US) emphasizes partnerships and collaborations to expand its product offerings, while John Deere (US) leverages its strong brand presence to drive digital transformation in farming equipment, thereby shaping a competitive environment that prioritizes technological integration and sustainability.In terms of business tactics, companies are increasingly localizing manufacturing and optimizing supply chains to enhance efficiency and responsiveness to market demands. The market structure appears moderately fragmented, with several players vying for market share, yet the collective influence of major companies like Bayer, Corteva, and John Deere is significant. Their strategies not only enhance their individual competitiveness but also contribute to a more robust market framework that encourages innovation and collaboration. In October Bayer (DE) announced a strategic partnership with a leading AI technology firm to develop advanced predictive analytics tools for crop management. This move is likely to enhance Bayer's capabilities in providing data-driven solutions to farmers, thereby reinforcing its commitment to innovation and sustainability. The partnership signifies a shift towards more integrated solutions that can potentially transform agricultural practices. In September Corteva Agriscience (US) launched a new AI-driven platform aimed at optimizing resource allocation in farming operations. This platform is designed to assist farmers in making informed decisions regarding water usage and fertilizer application, which could lead to significant cost savings and improved crop yields. The introduction of this platform underscores Corteva's focus on leveraging technology to address pressing agricultural challenges. In August John Deere (US) unveiled its latest line of smart tractors equipped with AI capabilities for real-time data analysis and decision-making. This innovation is expected to enhance operational efficiency and reduce labor costs for farmers. By integrating AI into its machinery, John Deere is positioning itself as a leader in the digital transformation of agriculture, which may set new standards for the industry. As of November the most prominent trends shaping competition in the applied ai-in-agriculture market include digitalization, sustainability, and the integration of AI technologies. Strategic alliances are increasingly becoming a cornerstone of competitive strategy, enabling companies to pool resources and expertise. Looking ahead, it appears that competitive differentiation will evolve from traditional price-based competition to a focus on innovation, technological advancement, and supply chain reliability. This shift may redefine market dynamics, compelling companies to invest in cutting-edge solutions that meet the evolving needs of the agricultural sector. ## Recent News & Developments Recent developments in the US Applied AI in Agriculture Market highlight significant advancements and increasing investments in technologies aimed at enhancing agricultural productivity. Companies such as FarmWise, John Deere, and PrecisionHawk are leading the charge with innovative solutions that utilize AI for precision farming, pest detection, and crop management. Current affairs reflect a growing trend towards sustainability, with firms like BASF and Bayer leveraging AI to improve yield while minimizing environmental impact. In recent months, Trimble announced a merger with Raven Industries in April 2023, enhancing its technology portfolio for agricultural productivity, with widespread media coverage confirming the move. Additionally, IBM's partnership with various agricultural firms aims to integrate AI into supply chain solutions, improving logistics and forecasting. Over the past two years, significant milestones have included Corteva's launch of AI-driven crop simulation tools in July 2022 and Ag Leader Technology increasing its market share through strategic enhancements in data analytics capabilities. Overall, the valuation of AI in agriculture is expected to grow as technology adoption accelerates, driving efficiency and sustainability in farming practices across the United States. ## Report Scope | MARKET SIZE 2024 | 655.61(USD Million) | | --- | --- | | MARKET SIZE 2025 | 748.58(USD Million) | | MARKET SIZE 2035 | 2820.47(USD Million) | | COMPOUND ANNUAL GROWTH RATE (CAGR) | 14.18% (2025 - 2035) | | REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends | | BASE YEAR | 2024 | | Market Forecast Period | 2025 - 2035 | | Historical Data | 2019 - 2024 | | Market Forecast Units | USD Million | | Key Companies Profiled | Bayer (DE), Corteva Agriscience (US), Syngenta (CH), John Deere (US), AG Leader Technology (US), Trimble (US), Taranis (IL), Farmers Edge (CA), Aker Solutions (NO) | | Segments Covered | Technology, Offering, Application | | Key Market Opportunities | Integration of precision agriculture technologies enhances yield and resource efficiency in the applied ai-in-agriculture market. | | Key Market Dynamics | Technological advancements drive efficiency and sustainability in the applied ai-in-agriculture market, reshaping competitive dynamics. | | Countries Covered | US | ## Frequently Asked Questions **Q: What is the current valuation of the US applied ai-in-agriculture market?** A: The market valuation was $655.61 Million in 2024. **Q: What is the projected market size for the US applied ai-in-agriculture market by 2035?** A: The market is projected to reach $2820.47 Million by 2035. **Q: What is the expected CAGR for the US applied ai-in-agriculture market during the forecast period 2025 - 2035?** A: The expected CAGR is 14.18% during the forecast period. **Q: Which technology segments are leading in the US applied ai-in-agriculture market?** A: Machine Learning, Computer Vision, and Predictive Analytics are key technology segments. **Q: What were the valuations for Machine Learning and Computer Vision in 2024?** A: Machine Learning was valued at $200.0 Million, while Computer Vision was at $180.0 Million. **Q: How does the valuation of AI-as-a-Service compare to Software in the market?** A: AI-as-a-Service was valued at $355.61 Million, whereas Software reached $300.0 Million. **Q: What applications are driving growth in the US applied ai-in-agriculture market?** A: Drone Analytics and Precision Farming are significant applications contributing to market growth. **Q: What were the valuations for Drone Analytics and Precision Farming in 2024?** A: Drone Analytics was valued at $150.0 Million, while Precision Farming was at $505.61 Million. **Q: Who are the key players in the US applied ai-in-agriculture market?** A: Key players include Bayer, Corteva Agriscience, Syngenta, John Deere, and others. **Q: What does the future hold for the US applied ai-in-agriculture market?** A: The market is expected to grow substantially, reaching $2820.47 Million by 2035. --- *This Markdown endpoint is provided for AI systems and LLM crawlers. 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