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

Global Applied AI in Agriculture Drivers:

• Growing used of IOT in the agriculture

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:

• Inexperience with high-tech machine learning solutions

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:

• Agricultural drones to increase the demand of market

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

• By Component

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.

• By Technology

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.

• By Application

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

• IBM Corporation;
• Microsoft;
• Deere & Company;
• AgEagle Aerial Systems Inc.;
• The Climate Corporation;
• Granular, Inc.; Descartes Labs, Inc.;
• Prospera Technologies;
• Taranis; aWhere Inc.;
• GAMAYA;
• ec2ce;
• PrecisionHawk;
• VineView;  
• Tule Technologies Inc., and others.

Global Applied AI in Agriculture Market Recent Developments

• In April 2023, IBM and Texas A&M AgriLife collaborated to provide farmers with water consumption insights, which can boost agricultural productivity while lowering economic and environmental expenses. Texas A&M AgriLife and IBM will deploy and grow Liquid Prep, a technology solution that helps farmers decide "when to water" in dry parts of the United States.
• In November 2022, DJI Agriculture has introduced the Mavic 3 Multispectral, featuring an advanced multispectral imaging system. This technology swiftly captures crucial crop growth data, enhancing the effectiveness of agricultural production across various applications in precision farming and environmental monitoring. This innovation is set to assist farmers globally in elevating the quality and efficiency of their crop production, ultimately reducing expenses and boosting income.
• In October 2022, Microsoft Research.AI has recently unveiled FarmVibes, an open-source initiative featuring a suite of machine-learning models and technologies tailored for sustainable agriculture. FarmVibes.AI encompasses advanced data processing techniques designed to seamlessly integrate various types of spatiotemporal and geographic data, including but not limited to weather data, satellite imagery, and drone footage.

Scope of the Global Applied AI in Agriculture Market Report

Applied AI in Agriculture Market Report Segmentation

Objectives of the Study

The objectives of the study are summarized in 5 stages. They are as mentioned below:

• Global Applied AI in Agriculture Market Size and Forecast: To identify and estimate the market size for the global applied AI in agriculture market segmented by component, by technology, by application& region and by value (in U.S. dollars). Also, to understand the consumption/ demand created by consumers of applied AI in agriculture between 2020 and 2032.
• Market Landscape and Trends: To identify and infer the drivers, restraints, opportunities, and challenges for the global applied AI in agriculture market
• Market Influencing Factors: To find out the factors which are affecting the sales of applied AI in agriculture among consumers
• Impact of COVID-19: To identify and understand the various factors involved in the global applied AI in agriculture market affected by the pandemic
• Company Profiling: To provide a detailed insight into the major companies operating in the market. The profiling will include the financial health of the company's past 2-3 years with segmental and regional revenue breakup, product offering, recent developments,