The AI in agriculture market refers to the sector that involves the application of artificial intelligence (AI) technologies and solutions to various facets of agriculture and farming practices. This market encompasses the development and implementation of AI-driven tools, software, and hardware that assist farmers and agricultural businesses in making data-driven decisions, optimizing resource allocation, automating tasks, and enhancing overall productivity in crop cultivation, livestock management, and other agricultural activities. AI in agriculture aims to revolutionize traditional farming practices by improving efficiency, sustainability, and yield, while also addressing challenges such as labor shortages, resource optimization, and environmental impact within the agricultural sector.

Some of the benefits of designing a Artificial Intelligence in Agriculture Market include:

• Increased Productivity: AI enables precision farming by providing data-driven insights into crop health, soil conditions, and pest infestations. This information helps farmers make more informed decisions, leading to improved productivity and higher crop yields.
• Cost Reduction: AI-powered automation can reduce labor costs by automating tasks such as planting, harvesting, and weeding. It also facilitates efficient resource allocation, which can lower input costs, making agriculture more cost-effective.
• Resource Optimization: AI helps in the precise application of resources such as water, fertilizers, and pesticides. This reduces waste and minimizes the environmental impact while ensuring optimal resource utilization.
• Remote Monitoring: AI-powered systems allow farmers to remotely monitor and manage their farms, providing real-time data on crop conditions and enabling timely interventions, even from a distance.
• Predictive Analytics: AI can analyze historical data to make predictions about future crop performance, weather patterns, and market conditions. This helps farmers plan more effectively and reduce risks.

Global Artificial Intelligence in Agriculture Market was valued at US $ 1.52 Billion in 2023 and is expected to reach US $ 10.9 Billion by 2032 growing at a CAGR of 24.5% during the forecast period 2024 – 2032.

COVID -19 Impact

The COVID-19 pandemic had a multifaceted impact on the AI in agriculture market. Supply chain disruptions hampered the production and distribution of AI-related components, causing delays and shortages. However, the pandemic-induced labor shortages and social distancing measures drove increased adoption of AI solutions to compensate for reduced workforce availability.

Remote monitoring and management of agricultural activities became paramount, spurring the use of AI-powered systems for remote control and surveillance. Economic uncertainty led to cautious spending by agricultural businesses, affecting some adoption plans. Nevertheless, a heightened focus on food security and supply chain resilience boosted investments in AI technologies to enhance agricultural efficiency and reduce vulnerabilities. Governments, recognizing the importance of agricultural technology, introduced support and incentives to drive AI adoption as part of their recovery strategies. In particular, AI-driven remote sensing and data analytics technologies gained prominence in assessing and predicting crop health and yield, allowing farmers to optimize resource allocation. As the agriculture sector continues to prioritize efficiency and resilience, AI is poised to play an increasingly vital role in shaping its future.

Factors Driving the Market

Drivers

Increase Use of Unmanned Aerial Vehicles (UAVs) Across Agricultural Farms

The growing utilization of Unmanned Aerial Vehicles (UAVs) across agricultural farms is a powerful driver for market growth. UAVs equipped with advanced sensors and cameras efficiently collect critical data regarding crops, soil conditions, and field attributes. This data empowers farmers to make informed decisions, optimizing resource allocation and enhancing crop health. Precision agriculture practices are made possible through UAVs, allowing for targeted and precise interventions, such as the application of fertilizers, pesticides, and irrigation, resulting in enhanced resource efficiency and increased crop yields.

Moreover, the adoption of UAVs leads to substantial cost savings by automating tasks, reducing the need for manual labor, and curbing operational expenses. Timely monitoring of agricultural areas by UAVs allows for swift issue identification and response, mitigating crop damage and losses, which leads to increased yields and reduced wastage. When coupled with AI and analytics, the data collected by UAVs enables data-driven decision-making, improving productivity and supporting sustainable agricultural practices. UAVs also contribute to environmental benefits through their ability to target the application of inputs, minimizing their environmental impact. This aligns with the growing trend towards sustainable and eco-friendly farming practices. The expanding use of UAVs not only enhances farm operations but also drives market growth by fostering demand for UAV technology and related services. It encourages innovation, competition, and technological advancements, further propelling the market forward.

Challenges

High cost of AI driven precision farming equipment

The high cost of AI-driven precision farming equipment exerts a multifaceted impact on market growth. It acts as a barrier to widespread adoption, particularly for smaller and financially constrained farmers, thereby constraining the market's expansion. Unequal access to advanced technology can emerge, with larger and more financially robust farms benefiting while smaller, resource-limited ones struggle to keep pace. This disparity in access can result in market fragmentation and exacerbate inequalities in the agricultural sector.

Additionally, concerns over the return on investment (ROI) can hinder adoption, as farmers may be hesitant to invest in expensive equipment without clear evidence of cost savings or increased yields. The prolonged timeline required to realize these benefits can delay market growth. High entry barriers may discourage new entrants and startups from participating in the market, limiting competition and innovation. This lack of competition can impede market development. However, the high costs can also serve as a catalyst for research and development efforts to create more cost-effective solutions, potentially disrupting the market by offering affordable alternatives. This, in turn, may put pressure on established players to adjust their pricing strategies. Government policies and subsidies play a pivotal role, as they can either alleviate or exacerbate the cost issue. Supportive policies can stimulate market growth, while a lack of incentives can hinder it.

Trends

Increasing adoption of drones in agricultural farms

The increasing adoption of drones in agricultural farms is catalyzing significant market growth. Drones offer an array of advantages in the agricultural sector. Their capabilities for precision farming, data collection, and monitoring enable farmers to make informed decisions, leading to optimized resource allocation, reduced costs, and increased crop yields. Timely surveillance of vast agricultural areas allows for swift issue identification and proactive responses, reducing crop damage and losses. When paired with AI and analytics, drone-captured data drives data-driven decision-making, improving productivity and sustainability. Furthermore, the targeted application of fertilizers and pesticides facilitated by drones contributes to environmental benefits. In sum, the burgeoning utilization of drones in agriculture is enhancing overall farming practices, bolstering efficiency, and propelling the agricultural technology market's growth.

Market Segmentation

By Offering

By Offering, the global Artificial Intelligence in Agriculture Market is divided into Hardware, Software, Services, Artificial Intelligence-as-a-Service.

The software category takes the lead in the AI in agriculture market, boasting a market share exceeding 52.43%. Software plays a pivotal role as the cornerstone of AI-powered solutions, facilitating data collection, analysis, informed decision-making, and task automation. Anticipated to sustain substantial growth, the software segment is poised for a compound annual growth rate (CAGR) of over 22.45% from 2024 to 2032.

Conversely, the services sector emerges as the swiftest-growing offering within the AI in agriculture market, with a projected CAGR exceeding 32.56% from 2023 to 2032. This surge is attributable to the essential role of service providers in aiding farmers with AI solution implementation and utilization. These providers cover a spectrum of assistance, encompassing data collection, analysis, training, and support to address the diverse needs of farmers.

By Deployment

By Deployment, the global Artificial Intelligence in Agriculture Market is divided into On premise, Cloud Based, Hybrid.

The predominant deployment method in the AI in agriculture market is cloud-based, commanding a market share exceeding 56%. Cloud-based deployment offers numerous advantages, including scalability, flexibility, and cost-efficiency, surpassing on-premises options.

Meanwhile, the most rapidly advancing deployment type is hybrid, foreseeing a robust compound annual growth rate (CAGR) of over 28.42% from 2024 to 2032. Hybrid deployment combines the strengths of both cloud-based and on-premises models, enabling organizations to tailor their AI applications to the environment that best suits their specific requirements.

By Technology

By Technology, the global Artificial Intelligence in Agriculture Market is divided into Predictive Analytics, Machine Learning, and Computer Vision.

Predictive analytics commands the lead in the AI in agriculture market, claiming a market share exceeding 44.56%. Employing AI to analyze data and forecast future events, predictive analytics empowers farmers to make well-informed decisions regarding key agricultural activities, including planting, irrigation, and harvesting schedules.

Machine learning takes the spotlight as the swiftest-growing technology within the AI in agriculture market, with a projected compound annual growth rate (CAGR) surpassing 29.24% from 2024 to 2032. Machine learning, a subset of AI, enables computers to learn from data without explicit programming. This advancement facilitates the creation of adaptive AI-powered solutions capable of adjusting to changing conditions and learning from their own experience.

By Application

By Application the Artificial Intelligence in Agriculture Market is divided by Livestock Monitoring, Precision Farming, Agriculture Robots, Livestock Monitoring, and Drone Analytics.

The integration of drone analytics and AI within agriculture holds vast potential with 37.53% market share. AI's ability to analyze drone-acquired data empowers farmers with data-driven decision-making, improved resource allocation, and heightened productivity. Consequently, drone analytics is poised to play a pivotal role in propelling the AI market in agriculture.

Drones equipped with high-resolution cameras and sensors capture extensive data pertaining to crops, soil conditions, and field attributes. This data, when coupled with AI-driven analytics, equips farmers with invaluable insights into crop health, nutrient levels, pest presence, and other factors impacting agricultural productivity. AI-powered drone analytics facilitate precision agriculture by offering intricate information about specific field areas. Through AI algorithms scrutinizing drone-captured data, farmers can pinpoint variances in crop growth, soil moisture levels, or pest populations. This targeted approach enables optimized resource deployment, encompassing precise applications of fertilizers, pesticides, or irrigation, resulting in heightened resource efficiency and increased crop yields.

AI-boosted drone analytics efficiently monitor vast agricultural regions. Instead of labor-intensive manual inspections, AI algorithms autonomously analyze drone-captured data, detecting areas requiring attention. This streamlines operations, curbs labor costs, and empowers farmers to make informed decisions based on precise and timely information.According to NASSCOM, India is poised to witness a remarkable growth in the agriculture sector, adding approximately USD 90 billion in value through data and AI technologies by 2025. When considering all sectors, artificial intelligence is projected to contribute around USD 500 billion to India's GDP by 2025.

However, The agriculture robots segment stands out as the most rapidly expanding application within the AI in agriculture market, with a projected impressive compound annual growth rate (CAGR) exceeding 39.43% from 2024 to 2032. Agriculture robots are becoming increasingly instrumental in automating a wide range of farm tasks, including weeding, harvesting, and milking. This automation not only leads to a reduction in labor costs but also enhances the overall efficiency of agricultural operations.

By Region

By region, the global Artificial Intelligence in Agriculture Market is divided into North America, Europe, APAC and Others. Others is further divided into Middle East, Africa and South America.

The North American artificial intelligence (AI) market in agriculture stands as a substantially leading region with 24.45% market share, within the broader agricultural technology sector, showcasing robust growth. The increasing adoption of AI technologies in agriculture is set to drive significant expansion in the years ahead. Factors contributing to this growth include the imperative for heightened productivity, growing demand for precision farming methods, and the presence of advanced infrastructure. North American farmers and agricultural entities are avidly embracing AI technologies to enhance efficiency, resource allocation, and decision-making processes. AI applications in the region's agriculture encompass precision agriculture, remote sensing, crop monitoring, predictive analytics, and automated farming systems. These technologies empower data-informed decisions, bolster yield, reduce costs, and manage risks effectively.

The North American AI market in agriculture is characterized by fruitful collaborations between technology providers, agricultural firms, research institutions, and startups. Such partnerships foster innovation, yielding AI solutions tailored to the unique requirements of the region's agriculture. Investments in AI startups and partnerships further stimulate market growth and technological advancement. Recognizing AI's potential in agriculture, North American governments have instigated supportive policies and initiatives. These include funding programs, research grants, and regulatory frameworks aimed at propelling AI adoption and innovation in agriculture. Such measures provide an enabling environment for AI market expansion, facilitating sustainable and resilient agricultural practices. In January 2023, the United States and the European Union initiated a collaboration to leverage artificial intelligence (AI) for enhancing agriculture, climate forecasting, emergency response, and the electric grid. This cooperation, now between the European Commission and the White House, underscores AI's role in fostering advancements and innovation.

On the other side, The Asia-Pacific region stands out as the most rapidly advancing AI in agriculture market, anticipating an impressive compound annual growth rate (CAGR) of 18.45% from 2024 to 2032. This remarkable surge is propelled by several key factors:

• Escalating Global Food Demand: A burgeoning population intensifies the call for more food production.
• Enhanced Agricultural Productivity Imperative: The imperative to enhance agricultural productivity to meet this surging demand.
• Government Investment: Ample government funding for AI in agriculture research and development initiatives.
• Digital Adoption: The increasing adoption of smartphones and mobile devices among farmers, facilitating the uptake of AI technology in the agricultural sector.

Competitive Landscape

The global Artificial Intelligence in Agriculture Market is consolidated with the presence of few major players contributing to the market revenue. This dominance of these major players is driven by their technological expertise, extensive resources, and established brand recognition. These companies typically offered comprehensive and diversified solutions to end use industries.

• Deere & Company

Deere & Company, an American corporation, specializes in producing agricultural machinery, heavy equipment, construction machinery, and forestry equipment, alongside offering financial services and related activities. Established by John Deere in 1837 in Grand Detour, Illinois, the company boasts a rich history of agricultural innovation. In 1837, John Deere's groundbreaking invention of the first commercially successful steel plow transformed agriculture, enabling more efficient cultivation of heavier soils.

• Bayer AG

Bayer AG, a prominent German multinational, ranks among the world's largest pharmaceutical companies. With its headquarters in Leverkusen, Bayer operates across various sectors, encompassing pharmaceuticals, consumer healthcare, agricultural chemicals, seeds, and biotechnology products. Additionally, it is a constituent of the EURO STOXX 50 stock market index.

• BASF SE

BASF SE, headquartered in Ludwigshafen, Germany, is a leading global chemical conglomerate and the world's largest chemical producer. It ranks among the top ten revenue-generating companies in Germany and boasts operations across five continents, employing a workforce of over 111,000 individuals. BASF, originally established in 1865 in Mannheim, Germany, under the name Badische Anilin- & Sodafabrik (BASF), commenced its journey with the production of aniline dye for textile coloring. Subsequently, BASF diversified its product portfolio to include a range of dyes and chemicals serving the agricultural and pharmaceutical industries.

• CNH Industrial N.V.
• Agco Corporation
• The Mosaic Company
• Corteva Agriscience
• Nutrien Limited
• Yara International ASA
• Bunge Limited
• Cargill Incorporated
• Archer Daniels Midland Company
• Louis Dreyfus Company
• Olam International Limited
• Wilmar International Limited
• COFCO Corporation
• China National Cereals
• Oils and Foodstuffs Corporation
• Elanco Animal Health Incorporated
• Zoetis Inc.
• Merck & Co., Inc.
• Hewlett Packard Enterprise Development LP
• Cisco Systems, Inc.
• IBM Corporation
• Microsoft
• Amazon Web Services Inc
• Oracle
• aWhere Inc.
• Descartes Labs, Inc.
• Wipro Limited.

Recent Developments

• In November 2022, DJI Agriculture unveiled the Mavic 3 Multispectral, featuring a multispectral imaging system for rapid crop growth data capture. This innovation is set to revolutionize precision agriculture and environmental monitoring, benefiting farmers worldwide by boosting production efficiency and profitability.
• In October 2022, Microsoft introduced FarmVibes, an open-source project from Microsoft Research.AI, offering machine-learning models and technology for sustainable agriculture. FarmVibes.AI incorporates data processing techniques to integrate various spatiotemporal and geographic data sources, including weather data and satellite and drone imagery.
• September 2022 witnessed AgroCares announcing a partnership with trinamiX, integrating their high-performance hardware into AgroCares' next-generation nutrient scanner solution. TrinamiX leverages mobile NIR spectroscopy, combining robust hardware and adaptable software for on-the-spot nutrient testing, facilitating informed decision-making across various industries.
• April 2023, a partnership between IBM and Texas A&M AgriLife was formed, aimed at offering farmers valuable water consumption insights. This collaboration seeks to enhance agricultural productivity and reduce both economic and environmental costs by implementing and expanding Liquid Prep, a technology solution assisting farmers in making informed decisions regarding optimal irrigation timing in arid regions of the United States.

Artificial Intelligence in Agriculture Market Scope

Table of Contents

1 INTRODUCTION OF GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET

1.1 Overview of the Market

1.2 Scope of Report

1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY

3.1 Data Mining

3.2 Validation

3.3 Primary Interviews

3.4 List of Data Sources

4 GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET OUTLOOK

4.1 Overview

4.2 Market Dynamics

4.2.1 Drivers

4.2.2 Restraints

4.2.3 Opportunities

4.3 Porters Five Force Model

4.3.1. Bargaining Power of Suppliers

4.3.2. Threat of New Entrants

4.3.3. Threat of Substitutes

4.3.4. Competitive Rivalry

4.3.5. Bargaining Power among Buyers

4.4 Value Chain Analysis

5 GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET, BY OFFERING

5.1 Overview

5.2 Hardware

5.3 Software

5.4 Services

5.5 AI as a Service

6 GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET, BY TECHNOLOGY

6.1 Overview

6.2 Predictive Analytics

6.3 Machine Learning

6.4 Computer Vision

7 GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET, BY DEPLOYMENT

7.1 Overview

7.2 On premise

7.3 Cloud based.

7.4 Hybrid

8 GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET, BY APPLICATION

8.1 Overview

8.2 Livestock Monitoring

8.3 Precision Farming

8.4 Agriculture Robots
8.5 Livestock Monitoring

8.6 Drone Analytics

9 GLOBAL ARTIFICIAL INTELLIGENCE IN AGRICULTURE MARKET, By REGION

9.1 North America

9.1.1 U.S.

9.1.2 Canada

9.2 Europe

9.2.1 Germany

9.2.3 U.K.

9.2.4 France

9.2.5 Rest of Europe

9.3 Asia Pacific

9.3.1 China

9.3.2 Japan

9.3.3 India

9.3.4 South Korea

9.3.5 Singapore

9.3.6 Malaysia

9.3.7 Australia

9.3.8 Thailand

9.3.9 Indonesia

9.3.10 Philippines

9.3.11 Rest of Asia Pacific

9.4 Others

9.4.1 Saudi Arabia

9.4.2 U.A.E.

9.4.3 South Africa

9.4.4 Egypt

9.4.5 Israel

9.4.6 Rest of Middle East and Africa (MEA)

9.4.7 Brazil

9.4.8 Argentina

9.4.9 Mexico

9.4.10 Rest of South America

10 COMPANY PROFILES

10.1 Deere & Company

10.1.1. Company Overview

10.1.2. Key Executives

10.1.3. Operating Business Segments

10.1.4. Product Portfolio

10.1.5. Financial Performance (As per availability)

10.1.6 Key News

10.2 Bayer AG

10.2.1. Company Overview

10.2.2. Key Executives

10.2.3. Operating Business Segments

10.2.4. Product Portfolio

10.2.5. Financial Performance (As per availability)

10.2.6. Key News

10.3 BASF SE

10.3.1. Company Overview

10.3.2. Key Executives

10.3.3. Operating Business Segments

10.3.4. Product Portfolio

10.3.5. Financial Performance (As per availability)

10.3.6. Key News

10.4  CNH Industrial N.V.

10.4.1. Company Overview

10.4.2. Key Executives

10.4.3. Operating Business Segments

10.4.4. Product Portfolio

10.4.5. Financial Performance (As per availability)

10.4.6. Key News

10.5 Agco Corporation

10.5.1. Company Overview

10.5.2. Key Executives

10.5.3. Operating Business Segments

10.5.4. Product Portfolio

10.5.5. Financial Performance (As per availability)

10.5.6. Key News

10.6 The Mosaic Company

10.6.1. Company Overview

10.6.2. Key Executives

10.6.3. Operating Business Segments

10.6.4. Product Portfolio

10.6.5. Financial Performance (As per availability)

10.6.6. Key News

10.7 Corteva Agriscience

10.7.1. Company Overview

10.7.2. Key Executives

10.7.3. Operating Business Segments

10.7.4. Product Portfolio

10.7.5. Financial Performance (As per availability)

10.7.6. Key News

10.8 Nutrien Limited

10.8.1. Company Overview

10.8.2. Key Executives

10.8.3. Operating Business Segments

10.8.4. Product Portfolio

10.8.5. Financial Performance (As per availability)

10.8.6. Key News

10.9 Yara International ASA

10.9.1. Company Overview

10.9.2. Key Executives

10.9.3. Operating Business Segments

10.9.4. Product Portfolio

10.9.5. Financial Performance (As per availability)

10.9.6. Key News

10.10 Bunge Limited

10.10.1. Company Overview

10.10.2. Key Executives

10.10.3. Operating Business Segments

10.10.4. Product Portfolio

10.10.5. Financial Performance (As per availability)

10.10.6. Key News

10.11 Cargill Incorporated

10.11.1. Company Overview

10.11.2. Key Executives

10.11.3. Operating Business Segments

10.11.4. Product Portfolio

10.11.5. Financial Performance (As per availability)

10.11.6. Key News

10.12 Archer Daniels Midland Company

10.12.1. Company Overview

10.12.2. Key Executives

10.12.3. Operating Business Segments

10.12.4. Product Portfolio

10.12.5. Financial Performance (As per availability)

10.12.6. Key News

10.13 Louis Dreyfus Company

10.13.1. Company Overview

10.13.2. Key Executives

10.13.3. Operating Business Segments

10.13.4. Product Portfolio

10.13.5. Financial Performance (As per availability)

10.13.6. Key News

10.14  Olam International Limited

10.14.1. Company Overview

10.14.2. Key Executives

10.14.3. Operating Business Segments

10.14.4. Product Portfolio

10.14.5. Financial Performance (As per availability)

10.14.6. Key News

10.15 Wilmar International Limited

10.15.1. Company Overview

10.15.2. Key Executives

10.15.3. Operating Business Segments

10.15.4. Product Portfolio

10.15.5. Financial Performance (As per availability)

10.15.6. Key News

10.16 COFCO Corporation

10.16.1. Company Overview

10.16.2. Key Executives

10.16.3. Operating Business Segments

10.16.4. Product Portfolio

10.16.5. Financial Performance (As per availability)

10.16.6. Key News

10.17 China National Cereals, Oils and Foodstuffs Corporation

10.17.1. Company Overview

10.17.2. Key Executives

10.17.3. Operating Business Segments

10.17.4. Product Portfolio

10.17.5. Financial Performance (As per availability)

10.17.6. Key News

10.18 Elanco Animal Health Incorporated

10.18.1. Company Overview

10.18.2. Key Executives

10.18.3. Operating Business Segments

10.18.4. Product Portfolio

10.18.5. Financial Performance (As per availability)

10.18.6. Key News

10.19 Zoetis Inc.

10.19.1. Company Overview

10.19.2. Key Executives

10.19.3. Operating Business Segments

10.19.4. Product Portfolio

10.19.5. Financial Performance (As per availability)

10.19.6. Key News

10.20 Merck & Co., Inc.

10.20.1. Company Overview

10.20.2. Key Executives

10.20.3. Operating Business Segments

10.20.4. Product Portfolio

10.20.5. Financial Performance (As per availability)

10.20.6. Key News

10.21Hewlett Packard Enterprise Development LP

10.21.1. Company Overview

10.21.2. Key Executives

10.21.3. Operating Business Segments

10.21.4. Product Portfolio

10.21.5. Financial Performance (As per availability)

10.21.6. Key News

10.22 Cisco Systems, Inc.

10.22.1. Company Overview

10.22.2. Key Executives

10.22.3. Operating Business Segments

10.22.4. Product Portfolio

10.22.5. Financial Performance (As per availability)

10.22.6. Key News

10.23 IBM Corporation

10.23.1. Company Overview

10.23.2. Key Executives

10.23.3. Operating Business Segments

10.23.4. Product Portfolio

10.23.5. Financial Performance (As per availability)

10.23.6. Key News

10.24 Microsoft

10.24.1. Company Overview

10.24.2. Key Executives

10.24.3. Operating Business Segments

10.24.4. Product Portfolio

10.24.5. Financial Performance (As per availability)

10.24.6. Key News

10.25 Amazon Web Services Inc.

10.25.1. Company Overview

10.25.2. Key Executives

10.25.3. Operating Business Segments

10.25.4. Product Portfolio

10.25.5. Financial Performance (As per availability)

10.20.6. Key News

10.26 Descartes Labs, Inc.

10.26.1. Company Overview

10.26.2. Key Executives

10.26.3. Operating Business Segments

10.26.4. Product Portfolio

10.26.5. Financial Performance (As per availability)

10.26.6. Key News

10.27 Oracle

10.27.1. Company Overview

10.27.2. Key Executives

10.27.3. Operating Business Segments

10.27.4. Product Portfolio

10.27.5. Financial Performance (As per availability)

10.27.6. Key News

10.28 Wipro Limited.

10.28.1. Company Overview

10.28.2. Key Executives

10.28.3. Operating Business Segments

10.28.4. Product Portfolio

10.28.5. Financial Performance (As per availability)

10.28.6. Key News

10.29 Granular, Inc.

10.29.1. Company Overview

10.29.2. Key Executives

10.29.3. Operating Business Segments

10.29.4. Product Portfolio

10.29.5. Financial Performance (As per availability)

10.29.6. Key News

10.30  aWhere Inc.

10.30.1. Company Overview

10.30.2. Key Executives

10.30.3. Operating Business Segments

10.30.4. Product Portfolio

10.30.5. Financial Performance (As per availability)

10.30.6. Key News

Global Artificial Intelligence in Agriculture Market Segmentation

Artificial Intelligence in Agriculture by Offering: Market Size & Forecast 2023-2030

• Hardware
• Software
• Services
• AI as a Service

Artificial Intelligence in Agriculture by Deployment: Market Size & Forecast 2023-2030

• On premise
• Cloud
• Hybrid

Artificial Intelligence in Agriculture by Technology: Market Size & Forecast 2023-2030

• Predictive Analytics
• Machine Learning
• Computer Vision

Artificial Intelligence in Agriculture by Application: Market Size & Forecast 2023-2030

• Livestock Monitoring
• Precision Farming
• Agriculture Robots
• Livestock Monitoring
• Drone Analytics

Artificial Intelligence in Agriculture by Geography: Market Size & Forecast 2023-2030

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

Major Players:

• Deere & Company
• Bayer AG
• BASF SE
• CNH Industrial N.V.
• Agco Corporation
• The Mosaic Company
• Corteva Agriscience
• Nutrien Limited
• Yara International ASA
• Bunge Limited
• Cargill Incorporated
• Archer Daniels Midland Company
• Louis Dreyfus Company
• Olam International Limited
• Wilmar International Limited
• COFCO Corporation
• China National Cereals
• Oils and Foodstuffs Corporation
• Elanco Animal Health Incorporated
• Zoetis Inc.
• Merck & Co., Inc.
• Hewlett Packard Enterprise Development LP
• Cisco Systems, Inc.
• IBM Corporation
• Microsoft
• Amazon Web Services Inc
• Oracle
• aWhere Inc.
• Descartes Labs, Inc.
• Wipro Limited.