Generative AI In Agriculture Market By Crop Type (Wheat, Rice, Corn, Vegetables, And Others), By Application (Precision Farming, Livestock Management, Crop Management, And Others), By Technology (Deep Learning, Computer Vision, Machine Learning, And Robotics), By Region And Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends And Forecast 2023-2032
-
37960
-
April 2024
-
285
-
-
This report was compiled by Correspondence Linkedin | Detailed Market research Methodology Our methodology involves a mix of primary research, including interviews with leading mental health experts, and secondary research from reputable medical journals and databases. View Detailed Methodology Page
-
Report Overview
The Global Generative AI in Agriculture Market size is expected to be worth around USD 1083.9 Mn by 2032 from USD 125 Mn in 2022, growing at a CAGR of 24.8% during the forecast period from 2023 to 2032.
The surge in demand for new advanced technologies and the emergence of the agricultural industry are some of the main driving factors for the generative use of artificial intelligence in the agricultural market. The agricultural market is undergoing a substantial change, with a growing emphasis on sustainability and efficacy.
GenAI is making a significant impact on the industry as it includes training machines to develop and produce data. By using GenAI, farmers can boost their crop yields by minimizing environmental impact and adopting sustainable farming practices.
Generative AI is a new and latest technology that uses data and many algorithms to understand, learn, forecast, and produce new content. GenAI in agriculture has the capability to transform farming practices by offering crop growth, soil conditions, and weather conditions. GenAI helps farmers increase resource allocations, decrease waste, and boost productivity.
It uses ML algorithms to examine a large amount of data. This data is then utilized to develop models that can produce new content like images, videos, and text. It also helps farmers forecast crop yield and growth, recognize potential disease outbreaks, and boost irrigation and fertilization methods.
India holds the second-largest agricultural land all over the world, providing employment for about half of the country’s population. According to the IBEF report published in August 2023, the food processing industry in India will account for 32% of the country’s total food market, which is one of the largest sectors in India and fifth in position with regards to production, consumption, and exports. The food grain industry in India will account for 330.5 million metric tons in 2022–2023.
In terms of the production of food grains, fruits, and vegetables, India is the second-largest producer and the second-largest exporter of sugar. A total of 521.27 LMT of rice has been projected for procurement for future KMS 2023–2024, up from 496 LMT produced during the last KMS 2022–2023.
In July 2022 to June 2023, India’s horticulture result is anticipated to account for a record of 350.87 million metric tons as production of fruits, vegetables, spices, and plantations of crops has drastically risen. Moreover, in October 2022, PM Mr. Narendra Modi inaugurated PM Kisan Samman Sammelan 2022 and released PM- Kisan funds worth Rs. 16,000 crores, which is USD 1.93 billion.
There are several advantages to implementing GenAI in agriculture, such as optimizing crop yield and quality, decreasing waste, and minimizing environmental impact. It also helps in increasing resource allocation, which consists of water and fertilizers.
Gen AI aids in enhancing disease identification and management. This technology also decreases risk and augments profitability. The demand for GenAI in agriculture will increase due to its surge in sustainability and high requirements in agriculture and the food industry, which will help in market expansion in the coming years.
Key Takeaways
- Market Value: The Generative AI in Agriculture Market is projected to reach USD 1083.9 million by 2032, exhibiting a robust CAGR of 24.8% from 2023 to 2032.
- Dominant Segments:
- Crop Type Analysis: Wheat leads with 36.7% market share, followed by rice, corn, vegetables, and other crop types, highlighting the importance of generative AI technologies in optimizing yields across diverse agricultural landscapes.
- Application Analysis: Precision farming dominates with a 35.4% market share, emphasizing the growing need for efficient and sustainable agricultural practices. Other critical applications include livestock management, crop management, soil analysis, and various emerging uses.
- Technology Analysis: Deep Learning holds the largest share at 37.2%, showcasing its efficacy in processing complex agricultural data. Computer Vision, Machine Learning, NLP, and Robotics also play vital roles in transforming farming operations.
- End-User Industry Analysis: Farmers represent the primary end-users, accounting for 42.3% of the market, followed by Agriculture Technology Companies, Agriculture Consultants, Government Agencies, and Research Institutions.
- Regional Dynamics: North America dominates with a 47.6% market share, driven by advanced technology adoption and robust agricultural practices. Other regions, including Europe, also contribute significantly to market growth.
- Key Players: Major players such as IBM Corp., Microsoft Corp., John Deere, and The Climate Corporation (a subsidiary of Bayer) lead the market, driving innovation and adoption of generative AI technologies in agriculture.
- Analyst Viewpoint: Analysts anticipate significant growth opportunities in Generative AI in Agriculture, fueled by increasing demand for precision farming, advancements in AI technologies, and the need for sustainable food production.
- Growth Opportunities: Opportunities lie in expanding applications of generative AI across different crop types, enhancing precision farming techniques, leveraging advanced technologies like Deep Learning and Robotics, and fostering collaboration between stakeholders to drive innovation and sustainability in agriculture.
Driving Factors
Automation and Efficiency Drives Market Growth
The integration of Generative AI into agriculture is revolutionizing how farming tasks are performed, leading to significant enhancements in automation and efficiency. This innovative technology facilitates the automation of critical agricultural operations such as crop monitoring, yield prediction, and pest detection. By utilizing AI algorithms, farmers are empowered to make informed, data-driven decisions that optimize crop yields and improve resource utilization. This shift not only contributes to more sustainable and efficient food production but also aligns with the broader objectives of enhancing operational productivity across the agricultural sector.
In November 2022, a notable advancement was made by DJI Agriculture with the launch of the Mavic 3 Multispectral model. This drone is designed to capture rapid, detailed images of crop growth, aiding in the increased production and quality of crops globally. It exemplifies how technological innovations are providing farmers with tools to reduce costs and maximize income, underscoring the pivotal role of automation and efficiency in the growth of the Generative AI in Agriculture Market. This progression towards more automated agricultural practices signals a transformative period where efficiency and technology converge to meet the demands of modern food production.
Precision Farming Catalyzes Market Expansion
Precision farming stands as a cornerstone in the evolution of agricultural practices, enabled by the integration of Generative AI with cutting-edge technologies such as the Internet of Things (IoT), drones, and satellite imagery. This synergy facilitates a revolutionized approach to farming, offering real-time, detailed analyses of soil conditions, moisture levels, and crop health. Such precise data enables farmers to tailor their practices to the exact needs of their crops, significantly optimizing resource usage and yields.
The ability to apply water, fertilizers, and pesticides with pinpoint accuracy not only minimizes waste but also contributes to environmental sustainability. This targeted approach ensures that resources are utilized efficiently, supporting the global push towards more sustainable agricultural practices. As precision farming becomes more prevalent, it serves as a critical driver for the Generative AI in Agriculture Market, showcasing the potential of technology to reshape traditional farming into a more efficient, productive, and environmentally friendly endeavor.
Technological Advancements Propel Market Forward
Continuous technological advancements in AI and machine learning are at the forefront of driving the Generative AI in Agriculture Market towards new heights. These advancements are pivotal in developing more sophisticated and accurate AI models that can process vast amounts of agricultural data. The insights and predictions generated from this data are invaluable, offering precise guidance for addressing the multifaceted challenges faced in agriculture today.
This evolution of AI technology enhances decision-making processes, enabling more effective solutions to emerge for increasing crop yields, managing resources, and mitigating risks associated with farming. The ripple effects of these technological advancements are widespread, influencing not just the immediate area of agriculture but also the broader ecosystem of food production and supply chain management. As AI and machine learning technologies continue to evolve, their role in transforming agriculture into a more data-driven, efficient, and sustainable industry becomes increasingly significant, marking a new era of growth and innovation in the market.
Restraining Factors
High Implementation Costs Restrains Market Growth
The deployment of generative AI technologies in the field of agriculture comes with high upfront costs. This includes the need for advanced hardware, sophisticated software solutions, sensors, and comprehensive data collection systems. For small-scale farmers and agricultural operations in resource-limited regions, these costs can be a significant barrier.
The financial burden associated with adopting these advanced technologies restricts their accessibility and widespread implementation. Consequently, the market's expansion is limited, as a large segment of potential users finds it challenging to leverage these innovations. This financial hurdle not only affects the adoption rate but also influences the pace at which the generative AI in Agriculture Market can grow, particularly in under-resourced areas.
Data Privacy Concerns Inhibit Market Growth
Data privacy and security emerge as major concerns in the adoption of AI technologies within the agricultural sector. The collection, processing, and analysis of sensitive agricultural data introduce risks related to unauthorized access and misuse. Ensuring robust data protection measures are in place is essential for maintaining trust among stakeholders and complying with regulatory standards.
However, the complexities and costs associated with securing agricultural data can deter farmers and agricultural businesses from adopting generative AI solutions. These concerns about data privacy not only challenge the market's expansion by limiting user trust but also impose additional operational challenges for providers of AI technologies. The need for enhanced security measures and the potential for regulatory scrutiny add layers of complexity, hindering the broader adoption and growth of generative AI in the agriculture market.
Crop Type Analysis
Wheat Leads with 36.7% Market Domination
In the Generative AI in Agriculture Market, crop type segmentation provides insights into the application of AI technologies across different types of crops. Wheat emerges as the dominant sub-segment, accounting for 36.7% of the market. This significant share can be attributed to wheat's essential role in global food security and the increasing demand for sustainable and efficient wheat production practices. Generative AI technologies are utilized to optimize wheat yields, enhance pest and disease management, and improve resource use efficiency, addressing the challenges of climate change and population growth.
Other notable segments include rice, corn, vegetables, and other crop types. Rice and corn, being staple foods for a large portion of the world's population, also see substantial investments in AI to increase production efficiency and sustainability. The vegetable segment benefits from AI in precision agriculture practices, such as yield optimization and disease detection, catering to the rising consumer demand for fresh and high-quality produce.
The "other crop types" segment encompasses a broad range of crops, each presenting unique challenges and opportunities for the application of generative AI technologies. This diversity highlights the market's potential for growth and innovation, driven by the need for customized AI solutions across different agricultural contexts.
The dominance of wheat in the Generative AI in Agriculture Market is indicative of the sector's focus on major staple crops. However, the inclusion and significance of other crop types underscore the versatility and expansive potential of generative AI technologies in agriculture. These technologies offer solutions tailored to the specific needs of different crops, enhancing overall market growth through diversified applications and innovations.
Application Analysis
Precision Farming Leads Generative AI in Agriculture Market with 35.4% Dominance
Within the Generative AI in Agriculture Market, application-based segmentation sheds light on the various uses of AI technologies in farming operations. Precision farming stands out as the dominant sub-segment, holding a 35.4% share of the market. This dominance is driven by the growing need for more efficient and sustainable agricultural practices. Precision farming leverages AI to analyze vast amounts of data from various sources, such as soil sensors, drones, and satellites, to make informed decisions on irrigation, fertilization, and pest management. By optimizing these aspects of agriculture, precision farming significantly enhances crop yields, reduces resource waste, and minimizes environmental impact.
Other critical segments include livestock management, crop management, soil analysis, and other applications. Livestock management uses AI for monitoring animal health and behavior, improving livestock productivity and welfare. Crop management benefits from AI in detecting diseases and pests early, allowing for timely interventions. Soil analysis, enhanced by AI, provides detailed information on soil health and nutrient levels, informing better soil management practices. The "other applications" segment encompasses a wide range of emerging AI uses in agriculture, from greenhouse automation to supply chain optimization, reflecting the technology's adaptability and potential for innovation.
The prominence of precision farming as the dominant application of Generative AI in Agriculture underscores the sector's shift towards more data-driven and efficient farming practices. This shift is essential in meeting the increasing global food demand sustainably. Meanwhile, the importance of other segments, such as livestock management and soil analysis, illustrates the comprehensive role of AI in transforming all aspects of agriculture. The diversity in applications highlights the vast potential of generative AI to address various agricultural challenges, promoting growth and innovation within the market.
Technology Analysis
Deep Learning Leads Generative AI in Agriculture Tech Market with 37.2% Dominance
In the Generative AI in Agriculture Market, technology segmentation underscores the foundational role of various AI technologies in transforming agricultural practices. Deep Learning emerges as the dominant technology, accounting for 37.2% of the market. This preeminence is attributed to deep learning's exceptional ability to process and analyze vast amounts of unstructured data, making it invaluable for applications like image recognition in pest detection and crop health monitoring. By enabling more accurate and efficient analyses, deep learning technologies significantly enhance decision-making processes in agriculture, leading to improved yields and resource management.
Other significant technologies include Computer Vision, Machine Learning, Natural Language Processing (NLP), and Robotics. Computer Vision is pivotal in automating tasks such as crop monitoring and disease identification, leveraging image and video data to deliver precise insights.
Machine Learning underpins various predictive models in agriculture, from yield prediction to climate impact assessments, facilitating a proactive approach to farming. NLP finds its utility in aggregating and interpreting vast datasets, including research papers and market reports, to inform better agricultural practices. Robotics, meanwhile, plays a crucial role in automating physical tasks like harvesting, planting, and weeding, directly impacting labor efficiency and precision.
Deep Learning's dominance in the market underscores the high value placed on technologies capable of processing complex, voluminous datasets to extract actionable insights. Concurrently, the roles of Computer Vision, Machine Learning, NLP, and Robotics highlight the multifaceted nature of technological advancements in agriculture, each contributing uniquely to increasing efficiency, sustainability, and productivity in the sector.
End-User Industry Analysis
Farmers Lead Generative AI Adoption in Agriculture Sector with 42.3% Dominance
The Generative AI in Agriculture Market is also analyzed based on the end-user industry, revealing the primary beneficiaries and users of these technologies. Farmers stand out as the dominant end-user group, representing 42.3% of the market. This dominance underscores the direct impact of generative AI technologies on improving agricultural productivity and sustainability at the ground level.
Farmers leverage AI for a variety of purposes, including optimizing planting schedules, improving pest and disease management, and enhancing crop yields through precision farming techniques. The adoption of AI by farmers signifies a shift towards more data-driven and efficient agricultural practices, aiming to meet the increasing global food demand sustainably.
Other critical end-user segments include Agriculture Technology Companies, Agriculture Consultants, Government Agencies, and Research Institutions. Agriculture Technology Companies play a pivotal role in developing and providing AI solutions tailored to agricultural needs, driving innovation in the sector. Agriculture Consultants use AI to offer more informed advice and strategies to their clients, enhancing the overall effectiveness of agricultural operations.
Government Agencies incorporate AI in policy-making and regulatory frameworks to support sustainable agricultural practices and food security. Research Institutions contribute to the advancement of AI in agriculture through cutting-edge research, pushing the boundaries of what's possible in agricultural science and technology.
Key Market Segments
Based on Crop Type
- Wheat
- Rice
- Corn
- Vegetables
- Other Crop Types
Based on Application
- Precision Farming
- Livestock Management
- Crop Management
- Soil Analysis
- Other Applications
Based on Technology
- Deep Learning
- Computer Vision
- Machine Learning
- Natural Language Processing
- Robotics
Based on End-User Industry
- Farmer
- Agriculture Technology Companies
- Agriculture Consultants
- Government Agencies
- Research Institutions
Growth Opportunities
Precision Farming Integration Offers Growth Opportunity
The integration of Generative AI with precision farming practices presents significant growth opportunities within the agricultural sector. By harnessing the power of AI to analyze vast amounts of data collected from sensors, satellites, and drones, farmers can make informed decisions that optimize the use of water, fertilizers, and pesticides.
This leads to higher crop yields and more sustainable farming practices. John Deere's See & Spray technology exemplifies this trend by using computer vision and AI to target herbicide application precisely, thereby reducing chemical usage and promoting healthier crops. Such innovations not only improve efficiency and productivity but also contribute to environmental sustainability, marking a major step forward in the evolution of farming practices.
Crop Yield Prediction Enhances Market Potential
Generative AI significantly boosts the agriculture market's potential through accurate crop yield predictions. These AI-driven forecasts enable farmers to plan more effectively, managing resources and schedules to optimize production.
By analyzing patterns in historical data, weather conditions, and soil quality, AI models offer predictions with impressive accuracy. This capability helps in better inventory management and contract negotiations, providing a solid foundation for operational and financial planning. The adoption of such technologies underscores a shift towards more data-centric and predictive approaches in agriculture, promising higher efficiency and profitability.
Automated Pest and Disease Detection Expands Market Reach
Automated pest and disease detection through Generative AI marks a transformative growth opportunity for the agriculture sector. Utilizing advanced image analysis, AI algorithms can quickly identify pest infestations or disease outbreaks by examining images captured by drones or smartphones. Early detection allows for swift interventions, significantly reducing potential crop damage and loss.
Companies like Taranis leverage deep learning to analyze crop imagery, detecting signs of stress before they escalate into larger issues. This not only enhances crop productivity but also reduces reliance on broad-spectrum chemical treatments, favoring more targeted and eco-friendly approaches. The adoption of these technologies expands the market's reach by offering scalable solutions that maintain crop health and yield.
Trending Factors
Rise of Smart Agriculture Initiatives Are Trending Factors
Smart agriculture initiatives are gaining momentum globally, significantly influencing the trend towards generative AI in agriculture. With the challenges of climate change, population growth, and limited resources pressing on the agricultural sector, governments, NGOs, and the private sector are increasingly turning to advanced technology solutions.
Generative AI stands out as a scalable and cost-effective option for enhancing productivity, sustainability, and resilience in farming. Its ability to provide actionable insights and automate complex decisions positions it as a central technology in the push towards smarter, more sustainable agricultural practices.
Integration of Internet of Things (IoT) Devices Are Trending Factors
The integration of Internet of Things (IoT) devices with generative AI systems is a prominent trend in the agricultural sector. Sensors, drones, and autonomous vehicles collect a wealth of real-time data on various environmental and crop conditions.
When this data is processed through generative AI algorithms, it enables precise, data-driven farming decisions. This technological synergy is revolutionizing agriculture by improving efficiency, reducing reliance on manual labor, and enhancing overall decision-making processes. As a result, the adoption of IoT and generative AI technologies is becoming increasingly prevalent, driving significant advancements in agricultural practices.
Regional Analysis
North America Dominates with 47.6% Market Share
North America's leading position in the Generative AI in Agriculture Market, with a 47.6% share, is propelled by several key factors. High levels of technological adoption, significant investments in research and development, and supportive government policies contribute to this dominance. The region's emphasis on innovation and sustainability in farming practices further fuels the integration of generative AI technologies.
The dynamics of North America's market are shaped by its advanced agricultural infrastructure, a strong ecosystem of tech companies, and a proactive approach to addressing agricultural challenges through technology. The presence of leading AI research institutions and tech giants facilitates the development and deployment of cutting-edge AI solutions in agriculture.
North America's influence in the Generative AI in Agriculture Market is expected to remain strong. Continuous investments in AI and the growing adoption of smart farming techniques are likely to further cement the region's leadership position. Moreover, the ongoing push for sustainable and efficient food production systems will drive innovation in generative AI applications.
Regional Market Shares:
- Europe: - Europe's market share is bolstered by its strong focus on sustainability and precision farming, supported by favorable regulations and initiatives.
- Asia Pacific: - The Asia Pacific region is experiencing rapid growth due to increasing technological adoption, particularly in countries like China and India, aimed at enhancing agricultural productivity.
- Middle East & Africa: While still developing, this region's market share is gradually increasing with the adoption of modern farming techniques to address food security and desertification challenges.
- Latin America: - Latin America's market share is growing, driven by the adoption of advanced agricultural technologies to improve crop yields and combat climatic challenges.
Key Regions and Countries
- North America
- The US
- Canada
- Mexico
- Western Europe
- Germany
- France
- The UK
- Spain
- Italy
- Portugal
- Ireland
- Austria
- Switzerland
- Benelux
- Nordic
- Rest of Western Europe
- Eastern Europe
- Russia
- Poland
- The Czech Republic
- Greece
- Rest of Eastern Europe
- APAC
- China
- Japan
- South Korea
- India
- Australia & New Zealand
- Indonesia
- Malaysia
- Philippines
- Singapore
- Thailand
- Vietnam
- Rest of APAC
- Latin America
- Brazil
- Colombia
- Chile
- Argentina
- Costa Rica
- Rest of Latin America
- Middle East & Africa
- Algeria
- Egypt
- Israel
- Kuwait
- Nigeria
- Saudi Arabia
- South Africa
- Turkey
- United Arab Emirates
- Rest of MEA
Key Players Analysis
In the Generative AI in Agriculture market, key players such as IBM Corp., Microsoft Corp., John Deere, The Climate Corporation, Ag Leader Technology, Trimble Inc., Prospera Technologies, Descartes Labs, Taranis, and Granular have made significant strides. Their contributions are pivotal in transforming agricultural practices through AI-driven insights, enhancing crop yield predictions, pest management, and climate adaptability.
IBM and Microsoft lead in providing foundational AI technologies and cloud services, supporting data-driven decision-making. John Deere and Trimble Inc. focus on precision agriculture, integrating AI into farm equipment and management systems to optimize operations. The Climate Corporation and Granular specialize in data analytics for climate and crop health monitoring, respectively, driving forward sustainability and efficiency.
Prospera Technologies and Taranis leverage AI for detailed field and crop monitoring, enabling precise interventions. Descartes Labs stands out for its geospatial analysis, offering unique perspectives on agricultural resources. Collectively, these key players and others in the sector are advancing the adoption of AI in agriculture, aiming for higher efficiency, sustainability, and food security, indicating a market rich in innovation and growth potential.
Market Key Players
- IBM Corp.
- Microsoft Corp.
- John Deere
- The Climate Corporation (a subsidiary of Bayer)
- Ag Leader Technology
- Trimble Inc.
- Prospera Technologies
- Descartes Labs
- Taranis
- Granular (a Corteva Agriscience company)
- Other Key Players
Recent Development
- April 2023 - With a brand new tool that makes use of an algorithm that generates AI to create new product concepts based upon insights from its platforms for spotting trends, Singapore-based Ai Palette is expanding its capabilities to help food and beverage companies speed up the process of developing for the creation of new products. In addition, it has completed the "missing part" between Ai Palette's latest concept screening instrument (Screen Winner) as well as its analytics platform that is powered by AI (Foresight Engine), the new concept generator for products is referred to as "Concept Genie," could dramatically speed up the development process.
- November 2022 - For a broad variety of applications within the fields of precision agriculture and environmental monitoring, DJI Agriculture introduces the Mavic 3 Multispectral model, equipped with an imaging system that rapidly captures the growth of crops to ensure higher yields in crop production. This device will aid farmers from all over the world in enhancing the efficiency and quality of their crops while also reducing costs and increasing the amount of income.
Report Scope:
Report Features Description Market Value (2022) USD 125 Mn Forecast Revenue (2032) USD 1083.9 Mn CAGR (2023-2032) 24.8% Base Year for Estimation 2022 Historic Period 2016-2022 Forecast Period 2023-2032 Report Coverage Revenue Forecast, Market Dynamics, COVID-19 Impact, Competitive Landscape, Recent Developments Segments Covered Based on Crop Type (Wheat, Rice, Corn, Vegetables, Other Crop Types)
Based on Application (Precision Farming, Livestock Management, Crop Management, Soil Analysis, Other Applications)
Based on Technology (Deep Learning, Computer Vision, Machine Learning, Natural Language Processing, Robotics)
Based on End-User Industry (Farmers, Agriculture Technology Companies, Agriculture Consultants, Government Agencies, Research Institutions)Regional Analysis North America – The US, Canada, & Mexico; Western Europe – Germany, France, The UK, Spain, Italy, Portugal, Ireland, Austria, Switzerland, Benelux, Nordic, & Rest of Western Europe; Eastern Europe – Russia, Poland, The Czech Republic, Greece, & Rest of Eastern Europe; APAC – China, Japan, South Korea, India, Australia & New Zealand, Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, & Rest of APAC; Latin America – Brazil, Colombia, Chile, Argentina, Costa Rica, & Rest of Latin America; Middle East & Africa – Algeria, Egypt, Israel, Kuwait, Nigeria, Saudi Arabia, South Africa, Turkey, United Arab Emirates, & Rest of MEA Competitive Landscape IBM Corp., Microsoft Corp., John Deere, The Climate Corporation (a subsidiary of Bayer), Ag Leader Technology, Trimble Inc., Prospera Technologies, Descartes Labs, Taranis, Granular (a Corteva Agriscience company), Other Key Players Customization Scope Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements. Purchase Options We have three licenses to opt for: Single User License, Multi-User License (Up to 5 Users), Corporate Use License (Unlimited User and Printable PDF) -
-
- IBM Corp.
- Microsoft Corp.
- John Deere
- The Climate Corporation (a subsidiary of Bayer)
- Ag Leader Technology
- Trimble Inc.
- Prospera Technologies
- Descartes Labs
- Taranis
- Granular (a Corteva Agriscience company)
- Other Key Players