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Artificial Intelligence in Agriculture Market Report Coverage

Artificial Intelligence in Agriculture Market Report Coverage

Base Year

2023

Market Size in 2023

$1,820.2 Million

Forecast Period

2024-2034

Value Projection and Estimation by 2034

$12,478.6 Million

CAGR During Forecast Period

18.95%

Number of Pages

140
Number of Tables 61

Number of Figures

 31

 

Key Market Players and Competition Synopsis

The companies that are profiled in the global artificial intelligence in agriculture market have been selected based on inputs gathered from primary experts and analyzing company coverage, product portfolio, and market penetration.

Some of the prominent names in this market are:

•    Deere & Company
•    IBM Corporation
•    Microsoft Corporation
•    Climate LLC
•    Alibaba Group Holding Limited
•    Aquabyte
•    Ceres Imaging
•    CNH Industrial N.V.
•    Connecterra B.V.
•    Cropin Technology Solutions Private Limited
•    DJI
•    Plantix
•    Blue River Technology
•    Farmers Edge

Companies that are not a part of the aforementioned pool have been well represented across different sections of the report (wherever applicable).

How can this report add value to an organization?

Product/Innovation Strategy: The product segment helps the reader understand the different products of global artificial intelligence in agriculture based on their use in agribusiness companies, agricultural cooperatives, government and private research institutes, and others. The technology types include drone scouting, mobile scouting, satellite imagery, and other advanced methods. The market is poised for significant expansion with ongoing technological advancements, increased investments, and growing awareness of the importance of precision agriculture. Therefore, the digital soil mapping business is a high-investment and high-revenue generating model.

Growth/Marketing Strategy: The global artificial intelligence in agriculture market has been growing rapidly. The market offers enormous opportunities for both existing and emerging players. Strategies covered in this segment include mergers and acquisitions, product launches, partnerships, collaborations, business expansions, and investments. Companies primarily focus on product development to maintain and strengthen their market positions.

Competitive Strategy: Key players in the global artificial intelligence in agriculture market analyzed and profiled in the study include technology developers and solution providers. Additionally, a comprehensive competitive landscape, including partnerships, agreements, and collaborations, is expected to aid the reader in understanding the untapped revenue pockets in the market.

Research Methodology

Factors for Data Prediction and Modeling

•    The scope of this report has been focused on artificial intelligence in agriculture applications and products.
•    The base currency considered for the market analysis is US$. Currencies other than the US$ have been converted to the US$ for all statistical calculations, considering the average conversion rate for that particular year.
•    The currency conversion rate has been taken from the historical exchange rate of the Oando website. 
•    Nearly all the recent developments from January 2020 to June 2024 have been considered in this research study.
•    The information rendered in the report is a result of in-depth primary interviews, surveys, and secondary analysis.
•    Where relevant information was not available, proxy indicators and extrapolation were employed.
•    Any economic downturn in the future has not been taken into consideration for the market estimation and forecast.
•    Technologies currently used are expected to persist through the forecast with no major breakthroughs in technology.

Market Estimation and Forecast

This research study involves the usage of extensive secondary sources, such as certified publications, articles from recognized authors, white papers, annual reports of companies, directories, and major databases to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of global artificial intelligence in agriculture market.

The process of market engineering involves the calculation of the market statistics, market size estimation, market forecast, market crackdown, and data triangulation (the methodology for such quantitative data processes is explained in further sections). The primary research study has been undertaken to gather information and validate the market numbers for segmentation types and industry trends of the key players in the market.

Primary Research

The primary sources involve industry experts from the global artificial intelligence in agriculture market and various stakeholders in the ecosystem. Respondents such as CEOs, vice presidents, marketing directors, and technology and innovation directors have been interviewed to obtain and verify both qualitative and quantitative aspects of this research study.

The key data points taken from primary sources include:

•    validation and triangulation of all the numbers and graphs
•    validation of reports segmentation and key qualitative findings
•    understanding the competitive landscape
•    validation of the numbers of various markets for market type
•    percentage split of individual markets for geographical analysis

Secondary Research

This research study of the global artificial intelligence in agriculture market involves the usage of extensive secondary research, directories, company websites, and annual reports. It also makes use of databases, such as Hoovers, Bloomberg, Businessweek, and Factiva, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the global market. In addition to the aforementioned data sources, the study has been undertaken with the help of other data sources and websites.

Secondary research was done in order to obtain crucial information about the industry’s value chain, revenue models, the market’s monetary chain, the total pool of key players, and the current and potential use cases and applications.

The key data points taken from secondary research include:

•    segmentations and percentage shares
•    data for market value
•    key industry trends of the top players of the market
•    qualitative insights into various aspects of the market, key trends, and emerging areas of innovation
•    quantitative data for mathematical and statistical calculations

 

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Artificial Intelligence in Agriculture Market - A Global and Regional Analysis

Focus on Application, Mode of Deployment, Product Type, and Region - Analysis and Forecast, 2024-2034

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Some Faq's

Frequently Asked Questions

AI technologies are being utilized for precision farming, predictive analytics, crop monitoring, and automated machinery, addressing critical challenges such as labor shortages, resource management, and climate variability. The integration of AI helps farmers make data-driven decisions, optimize resource use, and improve crop yields while reducing environmental impact. As technological advancements continue and the cost of AI solutions decreases, the adoption of AI in agriculture is expected to grow significantly, transforming traditional farming into a more intelligent and resilient sector.

AI is applied in the agriculture industry through various advanced technologies that enhance productivity, efficiency, and sustainability. It is used for precision farming, where AI algorithms analyze data from sensors, drones, and satellites to optimize planting, watering, and fertilization processes. AI-driven predictive analytics help forecast weather conditions, pest infestations, and crop diseases, enabling proactive measures to protect crops. Additionally, AI-powered robotics automate labor-intensive tasks such as harvesting and weeding, reducing reliance on manual labor. These applications not only improve crop yields and resource management but also contribute to sustainable farming practices by minimizing waste and environmental impact.

The future scope of AI in agriculture is vast and transformative, with the potential to revolutionize farming practices and address global food security challenges. AI technologies are expected to become increasingly integrated into all aspects of agricultural operations, from precision farming and automated machinery to advanced data analytics and predictive modeling. Innovations such as AI-driven drones for crop monitoring, machine learning algorithms for soil health assessment, and robotics for autonomous harvesting will enhance efficiency, productivity, and sustainability. Moreover, AI can facilitate better resource management, reduce environmental impacts, and improve resilience to climate change, ultimately leading to more resilient and adaptive agricultural systems capable of meeting the demands of a growing global population.

Smart agriculture in AI refers to the integration of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) to enhance farming practices. This approach involves using AI-driven tools and systems to collect, analyze, and interpret vast amounts of data from various sources such as soil sensors, weather stations, and satellite imagery. The insights derived from this data enable precise decision-making, optimizing agricultural processes such as irrigation, fertilization, pest control, and crop monitoring. By automating and improving these practices, smart agriculture aims to increase productivity, reduce waste, and promote sustainability in farming operations.

The following are some of the USPs of this report:

•    A dedicated section focusing on the trends adopted by the key players operating in the global artificial intelligence in agriculture market
•    Competitive landscape of the companies operating in the ecosystem offering a holistic view of the global artificial intelligence in agriculture market landscape 
•    Qualitative and quantitative analysis of the global artificial intelligence in agriculture market at the region and country level and granularity by application and product segments
•    Supply chain and value chain analysis

AI technology providers and agriculture cooperatives can buy this report.

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