[188 Pages Report] The Artificial Intelligence in Agriculture Market size was estimated at USD 2.25 billion in 2023 and expected to reach USD 2.73 billion in 2024, at a CAGR 22.45% to reach USD 9.29 billion by 2030.
Artificial intelligence (AI) techniques are significantly growing across the agriculture sector, which helps farmers to determine various parameters such as weed detection and yield detection and optimize the production and operation processes. These AI technologies allow farmers to recognize specific areas that need pesticide treatment, irrigation, and fertilization and harvest the crops without human error. In addition, these technologies save the excess use of water, pesticides, and herbicides, preserve soil fertility, and improve crop quality. Furthermore, the adoption of advanced unmanned aerial vehicles (UAVs) among farmers is significantly growing owing to their multiple features, such as capturing high-quality images and videos, which enables farmers to monitor the crops continuously. In addition, these unmanned aerial vehicles are assisting farmers in gathering data and tracking crops more effectively. These drones are widely used for various activities in agriculture, including crop mapping, soil analysis, irrigation, pest management, and increasing crop yield. However, the high cost of deployment of artificial intelligence and lack of awareness among farmers for AI in the agriculture sector is limiting the market growth. Moreover, introducing advanced farming technologies with artificial intelligence, such as big data and IoT, enables more precise farming practices, including irrigation, fertilizing, crop protection, and harvesting.
The advanced agriculture facilities and adoption of AI-based agriculture owing to the easy accessibility of AI-based agriculture across North America regions are contributing to the growth of artificial intelligence in the agriculture market. Additionally, U.S. government initiatives such as the farms of the future program, which aim to encourage farmers to adopt and integrate technology, are expected to create opportunities for artificial intelligence in the agriculture market across the Americas. Furthermore, SmartAgriHubs projects are encouraging farmers to adopt digital solutions to accelerate the digital transformation in the EU farming sector, majorly in rural areas, which is further expected to grow the market in this region. Emerging agriculture developments and government programs for the adoption of advanced technologies across the agriculture sector in various Asia Pacific countries, including India, China, and Vietnam, are subsequently expanding the market scope.
The market dynamics represent an ever-changing landscape of the Artificial Intelligence in Agriculture Market by providing actionable insights into factors, including supply and demand levels. Accounting for these factors helps design strategies, make investments, and formulate developments to capitalize on future opportunities. In addition, these factors assist in avoiding potential pitfalls related to political, geographical, technical, social, and economic conditions, highlighting consumer behaviors and influencing manufacturing costs and purchasing decisions.
- Market Drivers
- Growing trend of predictive analytics to enhance agricultural productivity
- Favorable government and private organizations initiatives to adopt modern agricultural technologies
- Escalating adoption of advanced unmanned aerial vehicles (UAVs), and satellite imaging systems across agriculture sector
- Market Restraints
- Constraints associated with the high cost of deployment of artificial intelligence (AI) in precision farming equipment
- Market Opportunities
- Emerging application of AI based agriculture in livestock monitoring and precision farming
- Integration of advanced farming technologies including big data, and IoT sensors with artificial intelligence
- Market Challenges
- Lack of awareness and limited availability of skilled professionals
The market disruption analysis delves into the core elements associated with market-influencing changes, including breakthrough technological advancements that introduce novel features, integration capabilities, regulatory shifts that could drive or restrain market growth, and the emergence of innovative market players challenging traditional paradigms. This analysis facilitates a competitive advantage by preparing players in the Artificial Intelligence in Agriculture Market to pre-emptively adapt to these market-influencing changes, enhances risk management by early identification of threats, informs calculated investment decisions, and drives innovation toward areas with the highest demand in the Artificial Intelligence in Agriculture Market.
The porter's five forces analysis offers a simple and powerful tool for understanding, identifying, and analyzing the position, situation, and power of the businesses in the Artificial Intelligence in Agriculture Market. This model is helpful for companies to understand the strength of their current competitive position and the position they are considering repositioning into. With a clear understanding of where power lies, businesses can take advantage of a situation of strength, improve weaknesses, and avoid taking wrong steps. The tool identifies whether new products, services, or companies have the potential to be profitable. In addition, it can be very informative when used to understand the balance of power in exceptional use cases.
The value chain of the Artificial Intelligence in Agriculture Market encompasses all intermediate value addition activities, including raw materials used, product inception, and final delivery, aiding in identifying competitive advantages and improvement areas. Critical path analysis of the <> market identifies task sequences crucial for timely project completion, aiding resource allocation and bottleneck identification. Value chain and critical path analysis methods optimize efficiency, improve quality, enhance competitiveness, and increase profitability. Value chain analysis targets production inefficiencies, and critical path analysis ensures project timeliness. These analyses facilitate businesses in making informed decisions, responding to market demands swiftly, and achieving sustainable growth by optimizing operations and maximizing resource utilization.
The pricing analysis comprehensively evaluates how a product or service is priced within the Artificial Intelligence in Agriculture Market. This evaluation encompasses various factors that impact the price of a product, including production costs, competition, demand, customer value perception, and changing margins. An essential aspect of this analysis is understanding price elasticity, which measures how sensitive the market for a product is to its price change. It provides insight into competitive pricing strategies, enabling businesses to position their products advantageously in the Artificial Intelligence in Agriculture Market.
The technology analysis involves evaluating the current and emerging technologies relevant to a specific industry or market. This analysis includes breakthrough trends across the value chain that directly define the future course of long-term profitability and overall advancement in the Artificial Intelligence in Agriculture Market.
The patent analysis involves evaluating patent filing trends, assessing patent ownership, analyzing the legal status and compliance, and collecting competitive intelligence from patents within the Artificial Intelligence in Agriculture Market and its parent industry. Analyzing the ownership of patents, assessing their legal status, and interpreting the patents to gather insights into competitors' technology strategies assist businesses in strategizing and optimizing product positioning and investment decisions.
The trade analysis of the Artificial Intelligence in Agriculture Market explores the complex interplay of import and export activities, emphasizing the critical role played by key trading nations. This analysis identifies geographical discrepancies in trade flows, offering a deep insight into regional disparities to identify geographic areas suitable for market expansion. A detailed analysis of the regulatory landscape focuses on tariffs, taxes, and customs procedures that significantly determine international trade flows. This analysis is crucial for understanding the overarching legal framework that businesses must navigate.
The regulatory framework analysis for the Artificial Intelligence in Agriculture Market is essential for ensuring legal compliance, managing risks, shaping business strategies, fostering innovation, protecting consumers, accessing markets, maintaining reputation, and managing stakeholder relations. Regulatory frameworks shape business strategies and expansion initiatives, guiding informed decision-making processes. Furthermore, this analysis uncovers avenues for innovation within existing regulations or by advocating for regulatory changes to foster innovation.
The FPNV positioning matrix is essential in evaluating the market positioning of the vendors in the Artificial Intelligence in Agriculture Market. This matrix offers a comprehensive assessment of vendors, examining critical metrics related to business strategy and product satisfaction. This in-depth assessment empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success, namely Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
The market share analysis is a comprehensive tool that provides an insightful and in-depth assessment of the current state of vendors in the Artificial Intelligence in Agriculture Market. By meticulously comparing and analyzing vendor contributions, companies are offered a greater understanding of their performance and the challenges they face when competing for market share. These contributions include overall revenue, customer base, and other vital metrics. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With these illustrative details, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
SLCM’s AI-Based App Agri Reach Gets NABL Accreditation
Sohan Lal Commodity Management Pvt. Ltd. (SLCM) Group, an agri-logistics solutions provider, received NABL accreditation for their AI-based app Agri Reach, which offers a range of services, including crop advisory, weather forecast, and market insights. This move is a game-changer in the ag-tech industry and could pave the way for more innovative solutions in the future. [Published On: 2023-03-18]
Wadhwani AI Signs MoU with Karnataka Government to Promote Welfare for Farmers
Wadhwani AI has signed a memorandum of understanding with the state government to promote farmers' welfare in Karnataka. This move is aimed at revolutionizing the agriculture sector in the region through the use of artificial intelligence. The application of AI in agriculture has the potential to transform the process of producing crops and address the existing challenges in the industry. By leveraging AI technologies, farmers can streamline their production processes, optimize their yields, and make informed decisions about crop management. [Published On: 2023-02-27]
Astanor Ventures Leads USD 23 Million Series A for Source.Ag's Greenhouse System
The recent Series A funding of USD 23 million into the agri-tech start-up Astanor Ventures is a major milestone in the advancement of artificial intelligence as it applies to agriculture. This investment will create an environment where farmers and growers can use data-driven solutions to maximize their yields while reducing resources and waste. Astanor's innovative approach has the potential to revolutionize how we grow fresh produce, providing a foundation for increased sustainability and efficiency in our agricultural industry. [Published On: 2023-02-22]
The strategic analysis is essential for organizations seeking a solid foothold in the global marketplace. Companies are better positioned to make informed decisions that align with their long-term aspirations by thoroughly evaluating their current standing in the Artificial Intelligence in Agriculture Market. This critical assessment involves a thorough analysis of the organization’s resources, capabilities, and overall performance to identify its core strengths and areas for improvement.
The report delves into recent significant developments in the Artificial Intelligence in Agriculture Market, highlighting leading vendors and their innovative profiles. These include Ag Code by Wilbur-Ellis Holdings, Inc., Bayer AG., AgEagle Aerial Systems Inc., Raven Industries, Inc., Wipro Limited, AgNext, Intello Labs Private Limited, Corteva Agriscience by Albaugh, LLC, Keenethics., Descartes Labs, Inc. by Antarctica Capital, PrecisionHawk Inc., Trace Genomics, Inc., John Deere Group, FarmBot, Inc., DeHaat by Green Agrevolution PVT. LTD, Intellias, LLC, AGCO Corporation, Apro Software, Gro Intelligence, Inc., Infosys Limited, Tule Technologies Inc., Gamaya Inc., Khetibuddy Agritech Private Limited., International Business Machines Corporation, CropX Technologies Ltd., Cainthus by Ever.Ag, Microsoft Corporation, ClimateAi, inc., Farmers Edge Inc., Trimble Inc., and Cropin Technology Solutions Pvt Ltd..
This research report categorizes the Artificial Intelligence in Agriculture Market to forecast the revenues and analyze trends in each of the following sub-markets:
- Offering
- Hardware
- Services
- Software
- Technology
- Computer Vision
- Machine Learning
- Predictive Analytics
- Deployment
- Cloud
- Hybrid
- On-premise
- Application
- Agriculture Robots
- Drone Analytics
- Labor Management
- Livestock Monitoring
- Precision Farming
- Region
- Americas
- Argentina
- Brazil
- Canada
- Mexico
- United States
- California
- Florida
- Illinois
- New York
- Ohio
- Pennsylvania
- Texas
- Asia-Pacific
- Australia
- China
- India
- Indonesia
- Japan
- Malaysia
- Philippines
- Singapore
- South Korea
- Taiwan
- Thailand
- Vietnam
- Europe, Middle East & Africa
- Denmark
- Egypt
- Finland
- France
- Germany
- Israel
- Italy
- Netherlands
- Nigeria
- Norway
- Poland
- Qatar
- Russia
- Saudi Arabia
- South Africa
- Spain
- Sweden
- Switzerland
- Turkey
- United Arab Emirates
- United Kingdom
- Americas
- Market Penetration: This section thoroughly overviews the current market landscape, incorporating detailed data from key industry players.
- Market Development: The report examines potential growth prospects in emerging markets and assesses expansion opportunities in mature segments.
- Market Diversification: This includes detailed information on recent product launches, untapped geographic regions, recent industry developments, and strategic investments.
- Competitive Assessment & Intelligence: An in-depth analysis of the competitive landscape is conducted, covering market share, strategic approaches, product range, certifications, regulatory approvals, patent analysis, technology developments, and advancements in the manufacturing capabilities of leading market players.
- Product Development & Innovation: This section offers insights into upcoming technologies, research and development efforts, and notable advancements in product innovation.
- What is the current market size and projected growth?
- Which products, segments, applications, and regions offer promising investment opportunities?
- What are the prevailing technology trends and regulatory frameworks?
- What is the market share and positioning of the leading vendors?
- What revenue sources and strategic opportunities do vendors in the market consider when deciding to enter or exit?
- Preface
- Research Methodology
- Executive Summary
- Market Overview
- Market Insights
- Artificial Intelligence in Agriculture Market, by Offering
- Artificial Intelligence in Agriculture Market, by Technology
- Artificial Intelligence in Agriculture Market, by Deployment
- Artificial Intelligence in Agriculture Market, by Application
- Americas Artificial Intelligence in Agriculture Market
- Asia-Pacific Artificial Intelligence in Agriculture Market
- Europe, Middle East & Africa Artificial Intelligence in Agriculture Market
- Competitive Landscape
- Competitive Portfolio
- List of Figures [Total: 24]
- List of Tables [Total: 380]
- List of Companies Mentioned [Total: 31]
Precision Farming:
Precision farming is the practice of optimizing crop yield while minimizing resources such as water, fertilizers, and pesticides through data-driven decision-making. AI technologies such as machine learning algorithms, sensors, and big data analytics can help farmers collect and analyze huge amounts of data in real-time, improving crop management. For example, AI-powered drones can collect data on crop health, soil moisture levels, and overall growth rates, enabling farmers to identify areas needing more irrigation, fertilizers, or other nutrients.
Livestock Monitoring:
Artificial Intelligence systems can also revolutionize livestock monitoring. This involves the use of sensors and other monitoring equipment to gather data on animal behavior, such as their specific needs for food, water, and shelter. With this information, farmers can optimize their breeding programs for better performance and reduce the risk of disease outbreaks. AI-powered monitoring systems can also help farmers identify sick animals early, providing an opportunity for intervention before the livestock becomes too ill to recover.
Automated Agriculture:
Another application of AI in agriculture is automating repetitive and labor-intensive processes such as planting, watering, and harvesting. Autonomous machines powered by AI, such as tractors and drones, can autonomously navigate through farms and perform these tasks with greater precision and efficiency. This reduces the physical burden on farmers and increases efficiency and accuracy, leading to higher yields and better-quality crops.
Food Supply Chain Management:
AI can also create value for agribusinesses in managing the food supply chain. Many challenges can be solved using AI, including the optimization of logistics, inventory management, and demand forecasting. AI algorithms can analyze weather and climate data, economic indicators, consumer trends, and other data to help farmers and agribusinesses plan better and improve supply chain efficiency.
Challenges and Opportunities:
Despite advances in AI and other related technologies, challenges remain for farmers to adopt the technology. The initial investment in purchasing AI-powered equipment and the related infrastructure can be extremely high, making the technology outside the reach of small-scale farmers. Increased awareness of the benefits of AI, government support, and innovative financial and credit solutions can help overcome these hurdles and make the technology more accessible.
Artificial Intelligence presents significant opportunities to revolutionize the agriculture industry. By adopting AI technologies in areas like precision farming, livestock monitoring, and food supply chain management, farmers can optimize resources, increase efficiency, and reduce costs. The use of AI also enables proactive decision-making by farmers and agribusinesses, improving the quality of crops and the health of livestock. While the technology has several challenges to overcome, including cost, infrastructure, and lack of awareness, its benefits are undeniable. AI for agriculture can help feed the growing global population in a sustainable and efficient manner, ultimately ensuring food security for years to come.
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