Global Applied AI in Agriculture Market Research Report: By Component (Hardware, Software, and Service), By Technology (Machine Learning & Deep Learning, Predictive Analytics, and Computer Vision), By Application (Precision Farming, Drone Analytics, Agriculture Robots, Livestock Monitoring, Others), and Region (North America, Europe, Asia-Pacific, and Rest of the World) Global Industry Analysis, Size, Share, Growth, Trends, Regional Analysis, Competitor Analysis and Forecast 2023-2031.
The global applied AI in agriculture market was valued at USD 2.09 billion in 2023 and is estimated to reach approximately USD 8.13 billion by 2031, at a CAGR of 21.63% from 2024 to 2032.
Applied AI in agriculture provides farmers with real-time crop insights, helping them to identify which areas need irrigation, fertilization, or pesticide treatment. Innovative farming practices such as vertical agriculture can also increase food production while minimizing resource usage. Furthermore, it used for the changing climates and increasing pollution, farmers can struggle to determine the right time to sow seeds.
APPLIED AI IN AGRICULTURE MARKET: REPORT SCOPE & SEGMENTATION
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Report Attribute |
Details |
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Estimated Market Value (2023) |
USD 2.09 Billion |
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Projected Market Value (2032) |
USD 8.13 Billion |
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Base Year |
2023 |
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Forecast Years |
2024- 2032 |
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Scope of the Report |
Historical and Forecast Trends, Industry Drivers and Constraints, Historical and Forecast Market Analysis by Segment- By Component, By Technology, By Application& Region |
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Segments Covered |
By Component, By Technology, By Application& Region |
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Forecast Units |
Value (USD Billion or Million), and Volume (Units) |
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Quantitative Units |
Revenue in USD million/billion and CAGR from 2023 to 2031 |
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Regions Covered |
North America, Europe, Asia Pacific, Latin America, and Middle East & Africa, and the Rest of World |
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Countries Covered |
U.S., Canada, Mexico, U.K., Germany, France, Italy, Spain, China, India, Japan, South Korea, Brazil, Argentina, GCC Countries, and South Africa, among others |
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Report Coverage |
Market growth drivers, restraints, opportunities, Porter’s five forces analysis, PEST analysis, value chain analysis, regulatory landscape, market attractiveness analysis by segments and region, company market share analysis, and COVID-19 impact analysis. |
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Delivery Format |
Delivered as an attached PDF and Excel through email, according to the purchase option. |
Global Applied AI in Agriculture Drivers:
Increasing amounts of data are generated in both structured and unstructured formats. These include historical weather patterns, soil reports, fresh studies, rainfall, insect invasion, and photos from drones and cameras. All of this data may be sensed by intellectual IOT systems, which can then give strong perceptions to boost yield. Proximity sensing and remote sensing are two technologies used mostly for intelligent data fusion. This aids in soil classification based on dirt beneath the surface in a specific location. Robot hardware solutions are already combining data collection software with robotics to provide the optimal fertilizer for growing corns as well as other operations to maximize production.
Restraints:
AI has huge potential for use in agriculture, there is currently a dearth of understanding of high-tech machine learning solutions in farms throughout most of the world. The exposure of farming to external elements such as weather, soil conditions, and the presence of pests is relatively significant. Similarly, AI systems necessitate a large amount of data in order to train algorithms and generate correct predictions. This is expected to hamper the growth of the global applied AI in agriculture market.
Trends:
The global population expected to reach over 9.8 billion by 2050, agricultural consumption is expected to rise by 75%, with drones now being mainstreamed for smart farming, assisting farmers in a variety of tasks ranging from analysis and planning to actual crop planting and subsequent field observing to determine health and growth. Drones equipped with hyperspectral, multispectral, or thermal sensors can also detect regions that require irrigation adjustments. Once the crops have begun to grow, these sensors can assess their vegetation index, a measure of health via AI, by determining the crop's heat signature.
Segment Overview
Based on component, the global applied AI in agriculture market is classified into hardware, software, service. The software segment dominated the market in 2023 and is expected to dominating during the applied AI in agriculture market in forecast period, due to AI-based solutions for agriculture are available from major firms such as IBM, Microsoft, and Deere & Company. By utilizing prediction-based analytics and computer vision, AI-based software increases crop productivity and yield. Furthermore, the increased adoption of predictive analytics-based software. Prominent predictive analytics-based software includes IBM Corporation's Watson Decision Platform, Microsoft's AI Sowing App, and Deere & Company's See and Spray pesticide and herbicide distribution systems. These artificial intelligence systems assist farmers in determining the best dates for crop sowing, detecting crop illnesses, monitoring crop production, and determining the quantity of land, fertilizer, water, and pesticides needed.
Based on technology, the global applied AI in agriculture market is divided into machine learning & deep learning, predictive analytics, computer vision. The predictive analytics segment dominated the global applied AI in agriculture market in 2023. This is attributed the implement artificial intelligence, several agriculture firms use predictive analytics. AgEagle Aerial Systems Inc., Microsoft, and Granular, Inc., for example, have collaborated on a prediction-based analytics technology to build AI-enabled farming and agriculture solutions and platforms. Moreover, drone-enabled agricultural solutions have been introduced to support predictive analytics. For Instance, AgEagle Aerial Systems Inc., which focuses on applying artificial intelligence to improve agriculture yield output, provides drone analytical solutions for identifying problem regions in crop fields and managing irrigation.
Based on application, the global applied AI in agriculture market is segmented into precision farming, drone analytics, agriculture robots, livestock monitoring, others. The precision farming segment dominated the applied AI in agriculture market with the largest revenue share in 2023. This due to Precision farming is one of the most rapidly increasing AI-enabled agricultural applications. It assists farmers in effectively minimizing expenses and optimizing resources. In addition, AI is used in precision farming to gather, interpret, and analyze digital data. For instance, GPS-enabled combine harvesters use artificial intelligence to measure harvest yield for field variability analysis, such as variances in water, soil makeup, or fungal, in order to generate georeferenced data. Therefore
Global Applied AI in Agriculture Overview by Region
North America dominated the applied AI in agriculture market in 2023. This is attributed to leading industrial automation industry and usage of artificial intelligence solutions in the region. North America is distinguished by increased purchasing power, ongoing advances in automation, significant investments in IIoT, and a growing emphasis on in-house AI equipment manufacture by governments. The presence of various agricultural technology suppliers developing artificial intelligence solutions, such as IBM Corporation, Deere & Company, Microsoft, Granular, Inc., and The Climate Corporation, further assists the market. As a result, rising growth of the applied AI in agriculture market in this region.
Global Applied AI in Agriculture Competitive Landscape
In the global applied AI in agriculture market, a few major players exert significant market dominance and have established a strong regional presence. These leading companies remain committed to continuous research and development endeavors and actively engage in strategic growth initiatives, including product development, launches, joint ventures, and partnerships. By pursuing these strategies, these companies aim to strengthen their market position, expand their customer base, and capture a substantial share of the market.
Some of the prominent players in the global applied AI in agriculture market include
Global Applied AI in Agriculture Market Recent Developments
Scope of the Global Applied AI in Agriculture Market Report
Applied AI in Agriculture Market Report Segmentation
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ATTRIBUTE |
DETAILS |
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By Component |
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By Technology |
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By Application |
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By Geography |
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Customization Scope |
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Pricing |
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Objectives of the Study
The objectives of the study are summarized in 5 stages. They are as mentioned below:
Research Methodology
Our research methodology has always been the key differentiating reason which sets us apart in comparison from the competing organizations in the industry. Our organization believes in consistency along with quality and establishing a new level with every new report we generate; our methods are acclaimed and the data/information inside the report is coveted. Our research methodology involves a combination of primary and secondary research methods. Data procurement is one of the most extensive stages in our research process. Our organization helps in assisting the clients to find the opportunities by examining the market across the globe coupled with providing economic statistics for each and every region. The reports generated and published are based on primary & secondary research. In secondary research, we gather data for global Market through white papers, case studies, blogs, reference customers, news, articles, press releases, white papers, and research studies. We also have our paid data applications which includes hoovers, Bloomberg business week, Avention, and others.
Data Collection
Data collection is the process of gathering, measuring, and analyzing accurate and relevant data from a variety of sources to analyze market and forecast trends. Raw market data is obtained on a broad front. Data is continuously extracted and filtered to ensure only validated and authenticated sources are considered. Data is mined from a varied host of sources including secondary and primary sources.
Primary Research
After the secondary research process, we initiate the primary research phase in which we interact with companies operating within the market space. We interact with related industries to understand the factors that can drive or hamper a market. Exhaustive primary interviews are conducted. Various sources from both the supply and demand sides are interviewed to obtain qualitative and quantitative information for a report which includes suppliers, product providers, domain experts, CEOs, vice presidents, marketing & sales directors, Type & innovation directors, and related key executives from various key companies to ensure a holistic and unbiased picture of the market.
Secondary Research
A secondary research process is conducted to identify and collect information useful for the extensive, technical, market-oriented, and comprehensive study of the market. Secondary sources include published market studies, competitive information, white papers, analyst reports, government agencies, industry and trade associations, media sources, chambers of commerce, newsletters, trade publications, magazines, Bloomberg BusinessWeek, Factiva, D&B, annual reports, company house documents, investor presentations, articles, journals, blogs, and SEC filings of companies, newspapers, and so on. We have assigned weights to these parameters and quantified their market impacts using the weighted average analysis to derive the expected market growth rate.
Top-Down Approach & Bottom-Up Approach
In the top – down approach, the Global Batteries for Solar Energy Storage Market was further divided into various segments on the basis of the percentage share of each segment. This approach helped in arriving at the market size of each segment globally. The segments market size was further broken down in the regional market size of each segment and sub-segments. The sub-segments were further broken down to country level market. The market size arrived using this approach was then crosschecked with the market size arrived by using bottom-up approach.
In the bottom-up approach, we arrived at the country market size by identifying the revenues and market shares of the key market players. The country market sizes then were added up to arrive at regional market size of the decorated apparel, which eventually added up to arrive at global market size.
This is one of the most reliable methods as the information is directly obtained from the key players in the market and is based on the primary interviews from the key opinion leaders associated with the firms considered in the research. Furthermore, the data obtained from the company sources and the primary respondents was validated through secondary sources including government publications and Bloomberg.
Market Analysis & size Estimation
Post the data mining stage, we gather our findings and analyze them, filtering out relevant insights. These are evaluated across research teams and industry experts. All this data is collected and evaluated by our analysts. The key players in the industry or markets are identified through extensive primary and secondary research. All percentage share splits, and breakdowns have been determined using secondary sources and verified through primary sources. The market size, in terms of value and volume, is determined through primary and secondary research processes, and forecasting models including the time series model, econometric model, judgmental forecasting model, the Delphi method, among Flywheel Energy Storage. Gathered information for market analysis, competitive landscape, growth trends, product development, and pricing trends is fed into the model and analyzed simultaneously.
Quality Checking & Final Review
The analysis done by the research team is further reviewed to check for the accuracy of the data provided to ensure the clients’ requirements. This approach provides essential checks and balances which facilitate the production of quality data. This Type of revision was done in two phases for the authenticity of the data and negligible errors in the report. After quality checking, the report is reviewed to look after the presentation, Type and to recheck if all the requirements of the clients were addressed.
