Artificial Intelligence In Agriculture
Artificial Intelligence in Agriculture Market Forecasts to 2030 - Global Analysis By Crop Type (Cereals & Grains, Oilseeds & Pulses, Fruits & Vegetables and Other Crop Types), Component, Deployment Mode, Technology, Application, End User and By Geography
According to Stratistics MRC, the Global Artificial Intelligence in Agriculture Market is accounted for $1.95 billion in 2024 and is expected to reach $6.53 billion by 2030 growing at a CAGR of 25.2% during the forecast period. Artificial Intelligence in agriculture refers to the application of machine learning, computer vision, robotics, and data analytics to enhance farming practices. AI-driven technologies enable precision farming by analyzing data from various sources such as soil sensors, weather forecasts, and satellite imagery. These technologies assist in optimizing crop yields, reducing resource usage, and minimizing environmental impact. Tasks such as pest detection, crop monitoring, and automated harvesting are streamlined through AI, leading to improved efficiency, sustainability, and profitability in agricultural operations.
According to NASSCOM, by 2025, approximately USD 90 billion of value will be added to the agriculture sector through data and AI technologies in India. With all the sectors combined, artificial intelligence is projected to add approximately USD 500 billion to India's GDP by 2025.
Market Dynamics:
Driver:
Increasing demand for food production
Increasing food production demand drives AI growth in agriculture by necessitating efficient resource use, yield maximization, and sustainable practices. AI technologies, such as precision farming, predictive analytics, and automated machinery, optimize resource use, improve crop yields, and reduce waste. As the global population rises, farmers adopt AI to meet food supply demands sustainably. Advanced AI applications facilitate real-time monitoring, pest and disease management, and data-driven decision-making, making agriculture more resilient and responsive to challenges, thereby propelling market growth.
Restraint:
Lack of technical expertise
The lack of technical expertise in Artificial Intelligence (AI) in agriculture stems from the sector's traditional reliance on conventional farming methods and limited exposure to advanced technologies. Insufficient technical know-how leads to underutilization of AI's potential, hindering innovation, data-driven decision-making and overall productivity improvements in agriculture. Consequently, the adoption rate of AI technologies slows, limiting the market's expansion and its transformative impact on the sector.
Opportunity:
Rising investments in agritech start-ups
Rising investments in agritech start-ups fosters innovation and development of advanced AI-driven solutions. These investments enable start-ups to enhance precision farming, optimize resource utilization, and improve crop yield through AI technologies like machine learning, computer vision, and data analytics. Increased funding accelerates research and development, leading to more robust and scalable AI applications, thereby transforming agricultural practices, boosting productivity, and addressing challenges such as climate change and food security.
Threat:
High initial investment costs
Artificial Intelligence in agriculture involves high initial investment costs due to the need for advanced technologies, infrastructure, and skilled personnel. Developing and implementing AI systems, such as machine learning algorithms, robotics, and IoT devices, requires substantial financial resources. Consequently, market growth is hampered as widespread implementation is slowed, creating a barrier to entry and reducing the overall pace of technological advancement and productivity improvements in the agricultural sector.
Covid-19 Impact
The covid-19 pandemic accelerated the adoption of AI in agriculture by highlighting the need for automation and resilience in food supply chains. Labor shortages and disrupted logistics spurred interest in AI-driven solutions for precision farming, remote monitoring, and automated harvesting. However, economic uncertainties and disrupted supply chains also posed challenges, affecting investment and implementation timelines for AI technologies in the agricultural sector.
The robotics & automation segment is expected to be the largest during the forecast period
The robotics & automation segment is estimated to have a lucrative growth. Robotics and automation in agriculture leverage AI to enhance efficiency and productivity. Autonomous tractors, drones, and robotic harvesters use AI for precision tasks like planting, watering, and harvesting. These technologies enable real-time monitoring and management of crops, reducing labor costs and increasing yields. AI-driven automation ensures optimal use of resources, minimizes waste, and helps in making data-driven decisions for better crop management and sustainability.
The field preparation segment is expected to have the highest CAGR during the forecast period
The field preparation segment is anticipated to witness the highest CAGR growth during the forecast period. Field preparation in AI-driven agriculture involves using technologies like soil sensors, drones, and machine learning algorithms to analyze soil health, moisture levels, and nutrient content. This data guides farmers in optimizing tillage, planting schedules, and soil treatment, leading to improved crop yields, reduced input costs, and sustainable farming practices. AI aids in precise field mapping and decision-making, enhancing overall efficiency and productivity in agriculture.
Region with largest share:
Asia Pacific is projected to hold the largest market share during the forecast period due to increasing food demand, government initiatives, and advancements in technology. Countries like China, India, and Japan are leading in adopting AI for precision farming, crop monitoring, and automated machinery. Rapid urbanization, technological advancements, and shifting dietary preferences are reshaping the market dynamics. The region's large agricultural base, coupled with rising investments in AgriTech start-ups, fosters innovation and implementation of AI solutions.
Region with highest CAGR:
Europe is projected to have the highest CAGR over the forecast period, driven by the region’s precision farming techniques. Europe is marked by a mix of small-scale family farms and large commercial operations, with an increasing focus on sustainability and organic production methods. Europe's supportive regulatory environment and government initiatives are highly promoting digital agriculture. This trend indicates a promising future for AI integration in European agriculture, poised to revolutionize the sector's operational landscape.
Key players in the market
Some of the key players profiled in the Artificial Intelligence in Agriculture Market include IBM Corporation, Microsoft Corporation, Deere & Company, Bayer AG, Trimble Inc., AG Leader Technology, Cropin Technology Solutions Pvt. Ltd., Agribotix LLC, Prospera Technologies, Descartes Labs, Taranis, Corteva, aWhere Inc., Ceres Imaging and Gamaya.
Key Developments:
In April 2024, Cropin launched Aksara, a generative AI system for climate smart agriculture. Aksara will cover nine crops such as paddy, wheat, maize, sorghum, barley, cotton, sugarcane, soybean, and millets for 5 countries in the Indian subcontinent. This generative AI system can suggest farmers which inputs to use for crops like rice or maize under specific agro-climatic conditions or provide climate smart agri-advisories, the company said in a statement.
In June 2023, Deere & Company has unveiled its first fully autonomous tractor, which is already operational on select farms and available for purchase. This tractor is a product of 20 years of AI development and is designed to complete tasks on time, every time, and at a high level of quality.
Crop Types Covered:
• Cereals & Grains
• Oilseeds & Pulses
• Fruits & Vegetables
• Other Crop Types
Components Covered:
• Hardware
• Software
• Services
Deployment Modes Covered:
• Cloud-Based
• On-Premises
Technologies Covered:
• Machine Learning
• Computer Vision
• Predictive Analytics
• Natural Language Processing (NLP)
• Robotics & Automation
• Other Technologies
Applications Covered:
• Precision Farming
• Livestock Monitoring
• Soil Management
• Field Preparation
• Other Applications
End Users Covered:
• Farmers
• Agribusinesses
• Research Organizations
• Government Bodies
• Other End Users
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global Artificial Intelligence in Agriculture Market, By Crop Type
5.1 Introduction
5.2 Cereals & Grains
5.3 Oilseeds & Pulses
5.4 Fruits & Vegetables
5.5 Other Crop Types
6 Global Artificial Intelligence in Agriculture Market, By Component
6.1 Introduction
6.2 Hardware
6.2.1 Sensors
6.2.2 Drones
6.2.3 Robots
6.3 Software
6.3.1 Artificial Intelligence Platforms
6.3.2 Artificial Intelligence Solutions
6.4 Services
6.4.1 Professional Services
6.4.2 Managed Services
7 Global Artificial Intelligence in Agriculture Market, By Deployment Mode
7.1 Introduction
7.2 Cloud-Based
7.3 On-Premises
8 Global Artificial Intelligence in Agriculture Market, By Technology
8.1 Introduction
8.2 Machine Learning
8.3 Computer Vision
8.4 Predictive Analytics
8.5 Natural Language Processing (NLP)
8.6 Robotics & Automation
8.7 Other Technologies
9 Global Artificial Intelligence in Agriculture Market, By Application
9.1 Introduction
9.2 Precision Farming
9.3 Livestock Monitoring
9.4 Soil Management
9.5 Field Preparation
9.6 Other Applications
10 Global Artificial Intelligence in Agriculture Market, By End User
10.1 Introduction
10.2 Farmers
10.3 Agribusinesses
10.4 Research Organizations
10.5 Government Bodies
10.6 Other End Users
11 Global Artificial Intelligence in Agriculture Market, By Geography
11.1 Introduction
11.2 North America
11.2.1 US
11.2.2 Canada
11.2.3 Mexico
11.3 Europe
11.3.1 Germany
11.3.2 UK
11.3.3 Italy
11.3.4 France
11.3.5 Spain
11.3.6 Rest of Europe
11.4 Asia Pacific
11.4.1 Japan
11.4.2 China
11.4.3 India
11.4.4 Australia
11.4.5 New Zealand
11.4.6 South Korea
11.4.7 Rest of Asia Pacific
11.5 South America
11.5.1 Argentina
11.5.2 Brazil
11.5.3 Chile
11.5.4 Rest of South America
11.6 Middle East & Africa
11.6.1 Saudi Arabia
11.6.2 UAE
11.6.3 Qatar
11.6.4 South Africa
11.6.5 Rest of Middle East & Africa
12 Key Developments
12.1 Agreements, Partnerships, Collaborations and Joint Ventures
12.2 Acquisitions & Mergers
12.3 New Product Launch
12.4 Expansions
12.5 Other Key Strategies
13 Company Profiling
13.1 IBM Corporation
13.2 Microsoft Corporation
13.3 Deere & Company
13.4 Bayer AG
13.5 Trimble Inc.
13.6 AG Leader Technology
13.7 Cropin Technology Solutions Pvt. Ltd.
13.8 Agribotix LLC
13.9 Prospera Technologies
13.10 Descartes Labs
13.11 Taranis
13.12 Corteva
13.13 aWhere Inc.
13.14 Ceres Imaging
13.15 Gamaya
List of Tables
1 Global Artificial Intelligence in Agriculture Market Outlook, By Region (2022-2030) ($MN)
2 Global Artificial Intelligence in Agriculture Market Outlook, By Crop Type (2022-2030) ($MN)
3 Global Artificial Intelligence in Agriculture Market Outlook, By Cereals & Grains (2022-2030) ($MN)
4 Global Artificial Intelligence in Agriculture Market Outlook, By Oilseeds & Pulses (2022-2030) ($MN)
5 Global Artificial Intelligence in Agriculture Market Outlook, By Fruits & Vegetables (2022-2030) ($MN)
6 Global Artificial Intelligence in Agriculture Market Outlook, By Other Crop Types (2022-2030) ($MN)
7 Global Artificial Intelligence in Agriculture Market Outlook, By Component (2022-2030) ($MN)
8 Global Artificial Intelligence in Agriculture Market Outlook, By Hardware (2022-2030) ($MN)
9 Global Artificial Intelligence in Agriculture Market Outlook, By Sensors (2022-2030) ($MN)
10 Global Artificial Intelligence in Agriculture Market Outlook, By Drones (2022-2030) ($MN)
11 Global Artificial Intelligence in Agriculture Market Outlook, By Robots (2022-2030) ($MN)
12 Global Artificial Intelligence in Agriculture Market Outlook, By Software (2022-2030) ($MN)
13 Global Artificial Intelligence in Agriculture Market Outlook, By Artificial Intelligence Platforms (2022-2030) ($MN)
14 Global Artificial Intelligence in Agriculture Market Outlook, By Artificial Intelligence Solutions (2022-2030) ($MN)
15 Global Artificial Intelligence in Agriculture Market Outlook, By Services (2022-2030) ($MN)
16 Global Artificial Intelligence in Agriculture Market Outlook, By Professional Services (2022-2030) ($MN)
17 Global Artificial Intelligence in Agriculture Market Outlook, By Managed Services (2022-2030) ($MN)
18 Global Artificial Intelligence in Agriculture Market Outlook, By Deployment Mode (2022-2030) ($MN)
19 Global Artificial Intelligence in Agriculture Market Outlook, By Cloud-Based (2022-2030) ($MN)
20 Global Artificial Intelligence in Agriculture Market Outlook, By On-Premises (2022-2030) ($MN)
21 Global Artificial Intelligence in Agriculture Market Outlook, By Technology (2022-2030) ($MN)
22 Global Artificial Intelligence in Agriculture Market Outlook, By Machine Learning (2022-2030) ($MN)
23 Global Artificial Intelligence in Agriculture Market Outlook, By Computer Vision (2022-2030) ($MN)
24 Global Artificial Intelligence in Agriculture Market Outlook, By Predictive Analytics (2022-2030) ($MN)
25 Global Artificial Intelligence in Agriculture Market Outlook, By Natural Language Processing (NLP) (2022-2030) ($MN)
26 Global Artificial Intelligence in Agriculture Market Outlook, By Robotics & Automation (2022-2030) ($MN)
27 Global Artificial Intelligence in Agriculture Market Outlook, By Other Technologies (2022-2030) ($MN)
28 Global Artificial Intelligence in Agriculture Market Outlook, By Application (2022-2030) ($MN)
29 Global Artificial Intelligence in Agriculture Market Outlook, By Precision Farming (2022-2030) ($MN)
30 Global Artificial Intelligence in Agriculture Market Outlook, By Livestock Monitoring (2022-2030) ($MN)
31 Global Artificial Intelligence in Agriculture Market Outlook, By Soil Management (2022-2030) ($MN)
32 Global Artificial Intelligence in Agriculture Market Outlook, By Field Preparation (2022-2030) ($MN)
33 Global Artificial Intelligence in Agriculture Market Outlook, By Other Applications (2022-2030) ($MN)
34 Global Artificial Intelligence in Agriculture Market Outlook, By End User (2022-2030) ($MN)
35 Global Artificial Intelligence in Agriculture Market Outlook, By Farmers (2022-2030) ($MN)
36 Global Artificial Intelligence in Agriculture Market Outlook, By Agribusinesses (2022-2030) ($MN)
37 Global Artificial Intelligence in Agriculture Market Outlook, By Research Organizations (2022-2030) ($MN)
38 Global Artificial Intelligence in Agriculture Market Outlook, By Government Bodies (2022-2030) ($MN)
39 Global Artificial Intelligence in Agriculture Market Outlook, By Other End Users (2022-2030) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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