AI in Agriculture Market Synopsis

The Global Market for Artificial Intelligence (AI) In Agriculture Was Estimated at USD 2.69 Billion In the Year 2023 and is Projected to Reach A Revised Size of USD 16.71 Billion By 2032, Growing at A CAGR of 22.5% Over the Forecast Period 2024- 2032.

Artificial Intelligence has become one of the most important technologies in every sector, including education, banking, robotics, agriculture, etc. In the agriculture sector, it is playing a very crucial role, and it is transforming the agriculture industry. Artificial Intelligence has improved crop production and real-time monitoring, harvesting, processing, and marketing.

• AI is widely used in this sector. For agriculture, Artificial Intelligence has become a revolutionary technology. It helps the farmers by yielding healthier crops, controlling pests, soil monitoring, and many more ways. The industry is turning to Artificial Intelligence technologies to help yield healthier crops, control pests, monitor soil, and growing conditions, organize data for farmers, help with the workload, and improve a wide range of agriculture-related tasks in the entire food supply chain.
• Different hi-tech computer-based systems are designed to determine various important parameters such as weed detection, yield detection, crop quality, and many more. AI has come up with a new application called Plantix. It was developed by PEAT to identify deficiencies in soil, including plant pests and diseases. With the help of this application, farmers can get an idea to use better fertilizer which can improve the harvest quality.
• With AI sensors, weed can be detected easily, and it also detects weed-affected areas. On finding such areas, herbicides can be precisely sprayed to reduce the use of herbicides and also saves time and crop. Different AI companies are building robots with AI and computer vision, which can precisely spray on weeds. The use of AI sprayers can widely reduce the number of chemicals to be used on fields, hence improving the quality of crops and also saving money. With help of Artificial Intelligence farmers can analyze weather conditions by using weather forecasting.
• Robotics is being widely used in different sectors, mainly in manufacturing, to perform complex tasks. Nowadays, different AI companies are developing robots to be employed in the Agriculture sector. These AI robots are developed in such a way that they can perform multiple tasks in farming.

AI In Agriculture Market Trend Analysis

Shortage of Labor Driving the Demand For AI

• There has always been an issue of labor shortage in the agriculture industry. AI can solve this issue with automation in farming. With AI and automation, farmers can get work done without having more people, and some examples are Driverless tractors.
• Smart irrigation and fertilizing systems, smart spraying, vertical farming software, and AI-based robots for harvesting. AI-driven machines and equipment are much faster and more accurate compared to human farmhands.
• The downturn is fueling trends toward automated farming operations because of a lack of qualified workers, aging farmers, and younger generations who don't find farming appealing.
• The developed world is not immune to this downward trend. The agricultural sector in Asia and the Pacific is experiencing a severe labor shortage due to an aging population. The market for artificial intelligence in agriculture is projected to flourish in the following years. ​

Increasing the Adoption of AI Solutions in Agriculture

• Climate change has made it difficult for farmers to determine the right time for sowing seeds, and that’s where AI comes into the picture. With the help of artificial intelligence, it is easy to gain insight into how weather, seasonal sunlight, wind speed, and rain will affect crop planting cycles.
• Technologies such as AI have the potential to anonymize data while helping to uncover insight that creates new opportunities. Farmers will no longer have to be worried about relinquishing their IP, which will encourage greater collaboration and ultimately growth.
• There is an opportunity for solution providers to concentrate on farms with less than 5 hectares of land because governments all over the world support the use of AI for agricultural applications and help farmers with small farms. For instance, the US Department of Agriculture offers small and mid-size producers’ programs that make easy loans available to farmers and enhance their technological know-how so they can use the best farming technology. Thus, the increasing adoption of AI solutions in agriculture is creating opportunities for AI in Agriculture Market.

Segmentation Analysis of The AI in Agriculture Market

AI in the Agriculture Market segments covers the Type, Technology, Deployment, Application, and end-users. By Application, the Precision Farming segment is Anticipated to Dominate the Market Over the Forecast period.

• Today, mobile apps, smart sensors, drones, and cloud computing make precision agriculture possible for farming cooperatives and even small family farms.
• Precision farming enables farmers to reduce costs and maximize available resources. For precision farming, digital data is gathered, interpreted, and analyzed by automated intelligence. Precision farming applications focus on increasing farm output because of the growing population and the need for better food quality which drives precision farming.
• Precision Agriculture leverages advanced digital technologies and will play a significant role in the third modern farming revolution. It effectively minimizes inputs, labor, and time sustainably, maximizes productivity and profitability, ensures sustainability, and reduces environmental impact. The Netherlands is a country known for advanced initiatives in precision farming
• Both big and small farmers and organizations working with growers benefit by adopting precision and digital farming technologies, which help to optimize agri-input resources without adding costs or workload.

Regional Analysis of The AI In Agriculture Market

North America is Expected to Dominate the Market Over the Forecast period.

• The North American economy is characterized by rising disposable income, ongoing funding for automation, significant investments in the Internet of Things, and an increasing focus on domestic AI equipment development by governments.
• The region has a thriving artificial intelligence market and a leading industrial automation sector. The population's increased purchasing power, ongoing increases in automation, large investments in the IoT, and growing government attention on domestic AI equipment production are all characteristics of North America.
• The presence of various agricultural technology companies looking into AI-based solutions, such as IBM Corporation, Deere & Company, Microsoft, Granular, Inc., and The Climate Corporation, is also advantageous to the market.      

Top Key Players Covered in The AI In Agriculture Market

• Deere & Company(US)
• IBM Corporation (US)
• Microsoft Corporation (US)
• The Climate Corporation (US)
• Farmers Edge Inc. (Canada)
• Hewlett Packard Enterprise Development LP (US)
• Cisco Systems Inc. (US)
• Google LLC (US)
• Amazon Web Services Inc (US)
• Corteva Inc (US)
• AgEagle Aerial Systems Inc. (US)
• Descartes Labs Inc. (US) and Other Major Players.

Key Industry Developments in the AI in Agriculture Market

• In June 2024, Cisco Investments, the venture arm of Cisco (NASDAQ: CSCO), launched a $1B AI investment fund to enhance the startup ecosystem and develop secure AI solutions. Initial investments include Cohere, Mistral AI, and Scale AI, aiming to boost customers’ AI readiness and support Cisco's AI innovation strategy. Nearly $200M of the fund has already been committed. This initiative underscores Cisco’s dedication to driving AI advancements and ensuring robust, reliable AI technologies for the future.
• In December 2023, John Deere is investing in AI for completely autonomous farms by the year 2030. John Deere is dedicating resources to AI technology, particularly in computer vision and machine learning, to transform the agricultural industry. CTO Jahmy Hindman believes that their See & Spray technology is a prime example of this innovation. The system accurately identifies and eliminates weeds using herbicide, minimizing excess and supporting healthier crop growth. By constantly analyzing field data, the system adjusts to different conditions and stages of plant growth, aiding farmers in addressing issues such as population growth and climate change. Deere's objective is to accomplish completely autonomous agriculture by 2030, improving both productivity and environmental friendliness.

Chapter 1: Introduction
 1.1 Research Objectives
 1.2 Research Methodology
 1.3 Research Process
 1.4 Scope and Coverage
  1.4.1 Market Definition
  1.4.2 Key Questions Answered
 1.5 Market Segmentation

Chapter 2:Executive Summary

Chapter 3:Growth Opportunities By Segment
 3.1 By By Type
 3.2 By Technology
 3.3 By Deployment
 3.4 By Application

Chapter 4: Market Landscape
 4.1 Porter's Five Forces Analysis
  4.1.1 Bargaining Power of Supplier
  4.1.2 Threat of New Entrants
  4.1.3 Threat of Substitutes
  4.1.4 Competitive Rivalry
  4.1.5 Bargaining Power Among Buyers
 4.2 Industry Value Chain Analysis
 4.3 Market Dynamics
  4.3.1 Drivers
  4.3.2 Restraints
  4.3.3 Opportunities
  4.5.4 Challenges
 4.4 Pestle Analysis
 4.5 Technological Roadmap
 4.6 Regulatory Landscape
 4.7 SWOT Analysis
 4.8 Price Trend Analysis
 4.9 Patent Analysis
 4.10 Analysis of the Impact of Covid-19
  4.10.1 Impact on the Overall Market
  4.10.2 Impact on the Supply Chain
  4.10.3 Impact on the Key Manufacturers
  4.10.4 Impact on the Pricing

Chapter 5: AI in Agriculture Market by By Type
 5.1 AI in Agriculture Market Overview Snapshot and Growth Engine
 5.2 AI in Agriculture Market Overview
 5.3 Solution
  5.3.1 Introduction and Market Overview
  5.3.2 Historic and Forecasted Market Size (2017-2032F)
  5.3.3 Key Market Trends, Growth Factors and Opportunities
  5.3.4 Solution: Geographic Segmentation
 5.4 Service
  5.4.1 Introduction and Market Overview
  5.4.2 Historic and Forecasted Market Size (2017-2032F)
  5.4.3 Key Market Trends, Growth Factors and Opportunities
  5.4.4 Service: Geographic Segmentation

Chapter 6: AI in Agriculture Market by Technology
 6.1 AI in Agriculture Market Overview Snapshot and Growth Engine
 6.2 AI in Agriculture Market Overview
 6.3 Machine Learning
  6.3.1 Introduction and Market Overview
  6.3.2 Historic and Forecasted Market Size (2017-2032F)
  6.3.3 Key Market Trends, Growth Factors and Opportunities
  6.3.4 Machine Learning: Geographic Segmentation
 6.4 Computer Vision
  6.4.1 Introduction and Market Overview
  6.4.2 Historic and Forecasted Market Size (2017-2032F)
  6.4.3 Key Market Trends, Growth Factors and Opportunities
  6.4.4 Computer Vision: Geographic Segmentation
 6.5 and Predictive Analytics
  6.5.1 Introduction and Market Overview
  6.5.2 Historic and Forecasted Market Size (2017-2032F)
  6.5.3 Key Market Trends, Growth Factors and Opportunities
  6.5.4 and Predictive Analytics: Geographic Segmentation

Chapter 7: AI in Agriculture Market by Deployment
 7.1 AI in Agriculture Market Overview Snapshot and Growth Engine
 7.2 AI in Agriculture Market Overview
 7.3 Cloud
  7.3.1 Introduction and Market Overview
  7.3.2 Historic and Forecasted Market Size (2017-2032F)
  7.3.3 Key Market Trends, Growth Factors and Opportunities
  7.3.4 Cloud: Geographic Segmentation
 7.4 On-premise
  7.4.1 Introduction and Market Overview
  7.4.2 Historic and Forecasted Market Size (2017-2032F)
  7.4.3 Key Market Trends, Growth Factors and Opportunities
  7.4.4 On-premise: Geographic Segmentation
 7.5 Hybrid
  7.5.1 Introduction and Market Overview
  7.5.2 Historic and Forecasted Market Size (2017-2032F)
  7.5.3 Key Market Trends, Growth Factors and Opportunities
  7.5.4 Hybrid: Geographic Segmentation

Chapter 8: AI in Agriculture Market by Application
 8.1 AI in Agriculture Market Overview Snapshot and Growth Engine
 8.2 AI in Agriculture Market Overview
 8.3 Weather Tracking
  8.3.1 Introduction and Market Overview
  8.3.2 Historic and Forecasted Market Size (2017-2032F)
  8.3.3 Key Market Trends, Growth Factors and Opportunities
  8.3.4 Weather Tracking: Geographic Segmentation
 8.4 Precision Farming
  8.4.1 Introduction and Market Overview
  8.4.2 Historic and Forecasted Market Size (2017-2032F)
  8.4.3 Key Market Trends, Growth Factors and Opportunities
  8.4.4 Precision Farming: Geographic Segmentation
 8.5 Drone Analytics
  8.5.1 Introduction and Market Overview
  8.5.2 Historic and Forecasted Market Size (2017-2032F)
  8.5.3 Key Market Trends, Growth Factors and Opportunities
  8.5.4 Drone Analytics: Geographic Segmentation

Chapter 9: Company Profiles and Competitive Analysis
 9.1 Competitive Landscape
  9.1.1 Competitive Positioning
  9.1.2 AI in Agriculture Sales and Market Share By Players
  9.1.3 Industry BCG Matrix
  9.1.4 Heat Map Analysis
  9.1.5 AI in Agriculture Industry Concentration Ratio (CR5 and HHI)
  9.1.6 Top 5 AI in Agriculture Players Market Share
  9.1.7 Mergers and Acquisitions
  9.1.8 Business Strategies By Top Players
 9.2 DEERE & COMPANY (US)
  9.2.1 Company Overview
  9.2.2 Key Executives
  9.2.3 Company Snapshot
  9.2.4 Operating Business Segments
  9.2.5 Product Portfolio
  9.2.6 Business Performance
  9.2.7 Key Strategic Moves and Recent Developments
  9.2.8 SWOT Analysis
 9.3 IBM CORPORATION (US)
 9.4 MICROSOFT CORPORATION (US)
 9.5 THE CLIMATE CORPORATION (US)
 9.6 FARMERS EDGE INC. (CANADA)
 9.7 HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP (US)
 9.8 CISCO SYSTEMS INC. (US)
 9.9 GOOGLE LLC (US)
 9.10 AMAZON WEB SERVICES INC (US)
 9.11 CORTEVA
 9.12 INC (US)
 9.13 AGEAGLE AERIAL SYSTEMS INC. (US)
 9.14 DESCARTES LABS INC. (US)
 9.15 OTHER
MAJOR PLAYERS

Chapter 10: Global AI in Agriculture Market Analysis, Insights and Forecast, 2017-2032
 10.1 Market Overview
 10.2 Historic and Forecasted Market Size By By Type
  10.2.1 Solution
  10.2.2 Service
 10.3 Historic and Forecasted Market Size By Technology
  10.3.1 Machine Learning
  10.3.2 Computer Vision
  10.3.3 and Predictive Analytics
 10.4 Historic and Forecasted Market Size By Deployment
  10.4.1 Cloud
  10.4.2 On-premise
  10.4.3 Hybrid
 10.5 Historic and Forecasted Market Size By Application
  10.5.1 Weather Tracking
  10.5.2 Precision Farming
  10.5.3 Drone Analytics

Chapter 11: North America AI in Agriculture Market Analysis, Insights and Forecast, 2017-2032
 11.1 Key Market Trends, Growth Factors and Opportunities
 11.2 Impact of Covid-19
 11.3 Key Players
 11.4 Key Market Trends, Growth Factors and Opportunities
 11.4 Historic and Forecasted Market Size By By Type
  11.4.1 Solution
  11.4.2 Service
 11.5 Historic and Forecasted Market Size By Technology
  11.5.1 Machine Learning
  11.5.2 Computer Vision
  11.5.3 and Predictive Analytics
 11.6 Historic and Forecasted Market Size By Deployment
  11.6.1 Cloud
  11.6.2 On-premise
  11.6.3 Hybrid
 11.7 Historic and Forecasted Market Size By Application
  11.7.1 Weather Tracking
  11.7.2 Precision Farming
  11.7.3 Drone Analytics
 11.8 Historic and Forecast Market Size by Country
  11.8.1 US
  11.8.2 Canada
  11.8.3 Mexico

Chapter 12: Eastern Europe AI in Agriculture Market Analysis, Insights and Forecast, 2017-2032
 12.1 Key Market Trends, Growth Factors and Opportunities
 12.2 Impact of Covid-19
 12.3 Key Players
 12.4 Key Market Trends, Growth Factors and Opportunities
 12.4 Historic and Forecasted Market Size By By Type
  12.4.1 Solution
  12.4.2 Service
 12.5 Historic and Forecasted Market Size By Technology
  12.5.1 Machine Learning
  12.5.2 Computer Vision
  12.5.3 and Predictive Analytics
 12.6 Historic and Forecasted Market Size By Deployment
  12.6.1 Cloud
  12.6.2 On-premise
  12.6.3 Hybrid
 12.7 Historic and Forecasted Market Size By Application
  12.7.1 Weather Tracking
  12.7.2 Precision Farming
  12.7.3 Drone Analytics
 12.8 Historic and Forecast Market Size by Country
  12.8.1 Bulgaria
  12.8.2 The Czech Republic
  12.8.3 Hungary
  12.8.4 Poland
  12.8.5 Romania
  12.8.6 Rest of Eastern Europe

Chapter 13: Western Europe AI in Agriculture Market Analysis, Insights and Forecast, 2017-2032
 13.1 Key Market Trends, Growth Factors and Opportunities
 13.2 Impact of Covid-19
 13.3 Key Players
 13.4 Key Market Trends, Growth Factors and Opportunities
 13.4 Historic and Forecasted Market Size By By Type
  13.4.1 Solution
  13.4.2 Service
 13.5 Historic and Forecasted Market Size By Technology
  13.5.1 Machine Learning
  13.5.2 Computer Vision
  13.5.3 and Predictive Analytics
 13.6 Historic and Forecasted Market Size By Deployment
  13.6.1 Cloud
  13.6.2 On-premise
  13.6.3 Hybrid
 13.7 Historic and Forecasted Market Size By Application
  13.7.1 Weather Tracking
  13.7.2 Precision Farming
  13.7.3 Drone Analytics
 13.8 Historic and Forecast Market Size by Country
  13.8.1 Germany
  13.8.2 UK
  13.8.3 France
  13.8.4 Netherlands
  13.8.5 Italy
  13.8.6 Russia
  13.8.7 Spain
  13.8.8 Rest of Western Europe

Chapter 14: Asia Pacific AI in Agriculture Market Analysis, Insights and Forecast, 2017-2032
 14.1 Key Market Trends, Growth Factors and Opportunities
 14.2 Impact of Covid-19
 14.3 Key Players
 14.4 Key Market Trends, Growth Factors and Opportunities
 14.4 Historic and Forecasted Market Size By By Type
  14.4.1 Solution
  14.4.2 Service
 14.5 Historic and Forecasted Market Size By Technology
  14.5.1 Machine Learning
  14.5.2 Computer Vision
  14.5.3 and Predictive Analytics
 14.6 Historic and Forecasted Market Size By Deployment
  14.6.1 Cloud
  14.6.2 On-premise
  14.6.3 Hybrid
 14.7 Historic and Forecasted Market Size By Application
  14.7.1 Weather Tracking
  14.7.2 Precision Farming
  14.7.3 Drone Analytics
 14.8 Historic and Forecast Market Size by Country
  14.8.1 China
  14.8.2 India
  14.8.3 Japan
  14.8.4 South Korea
  14.8.5 Malaysia
  14.8.6 Thailand
  14.8.7 Vietnam
  14.8.8 The Philippines
  14.8.9 Australia
  14.8.10 New Zealand
  14.8.11 Rest of APAC

Chapter 15: Middle East & Africa AI in Agriculture Market Analysis, Insights and Forecast, 2017-2032
 15.1 Key Market Trends, Growth Factors and Opportunities
 15.2 Impact of Covid-19
 15.3 Key Players
 15.4 Key Market Trends, Growth Factors and Opportunities
 15.4 Historic and Forecasted Market Size By By Type
  15.4.1 Solution
  15.4.2 Service
 15.5 Historic and Forecasted Market Size By Technology
  15.5.1 Machine Learning
  15.5.2 Computer Vision
  15.5.3 and Predictive Analytics
 15.6 Historic and Forecasted Market Size By Deployment
  15.6.1 Cloud
  15.6.2 On-premise
  15.6.3 Hybrid
 15.7 Historic and Forecasted Market Size By Application
  15.7.1 Weather Tracking
  15.7.2 Precision Farming
  15.7.3 Drone Analytics
 15.8 Historic and Forecast Market Size by Country
  15.8.1 Turkey
  15.8.2 Bahrain
  15.8.3 Kuwait
  15.8.4 Saudi Arabia
  15.8.5 Qatar
  15.8.6 UAE
  15.8.7 Israel
  15.8.8 South Africa

Chapter 16: South America AI in Agriculture Market Analysis, Insights and Forecast, 2017-2032
 16.1 Key Market Trends, Growth Factors and Opportunities
 16.2 Impact of Covid-19
 16.3 Key Players
 16.4 Key Market Trends, Growth Factors and Opportunities
 16.4 Historic and Forecasted Market Size By By Type
  16.4.1 Solution
  16.4.2 Service
 16.5 Historic and Forecasted Market Size By Technology
  16.5.1 Machine Learning
  16.5.2 Computer Vision
  16.5.3 and Predictive Analytics
 16.6 Historic and Forecasted Market Size By Deployment
  16.6.1 Cloud
  16.6.2 On-premise
  16.6.3 Hybrid
 16.7 Historic and Forecasted Market Size By Application
  16.7.1 Weather Tracking
  16.7.2 Precision Farming
  16.7.3 Drone Analytics
 16.8 Historic and Forecast Market Size by Country
  16.8.1 Brazil
  16.8.2 Argentina
  16.8.3 Rest of SA

Chapter 17 Investment Analysis

Chapter 18 Analyst Viewpoint and Conclusion