Home Technology AI in Agriculture Market Size, Share Analysis, Growth and Forecast to 2032

AI in Agriculture Market

AI in Agriculture Market Size, Share & Trends Analysis Report By Component (Hardware, Software, Services), By Technology (Machine Learning & Deep Learning, Predictive Analytics, Computer Vision), By Application (Precision Farming, Drone Analytics, Agriculture Robots, Livestock Monitoring, Others) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2024-2032

Report Code: SRTE65DR
Study Period 2020-2032 CAGR 20.2%
Historical Period 2020-2022 Forecast Period 2024-2032
Base Year 2023 Base Year Market Size USD 1.63 billion
Forecast Year 2032 Forecast Year Market Size USD 7.15 billion
Largest Market North America Fastest Growing Market Asia-Pacific
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Market Overview

The global AI in agriculture market size was valued at USD 1.63 billion in 2023. It is projected to reach USD 7.15 billion by 2032, growing at a CAGR of 20.2% during the forecast period (2024–2032). Artificial intelligence (AI) in agriculture uses cognitive technology to improve farming's capacity for learning, reasoning, understanding, and interaction. 

Artificial intelligence (AI) is now commonly used in the agricultural sector to increase crop yields without sacrificing quality. Farmers' attention has shifted from conventional farming techniques to refining the product through enhanced farming methods like drones, automated systems, and robots due to the increased research and development of sophisticated robotics technology in the agriculture industry. As a result, farmers now employ more advanced techniques. Due to rising demand, farmers are investing in more efficient farming methods, which necessitate implementing automated farming systems.

Market Dynamics

Global AI in Agriculture Market Drivers

Rising Need for Real-Time Livestock Monitoring

One of the most crucial trends in artificial intelligence in agriculture is the growing requirement for livestock monitoring. Dairy farms may now individually monitor all behavioral characteristics of a herd using advanced AI technologies such as facial recognition for animals and picture classification along with body condition score and feeding patterns. It has the potential to cause a revolutionary shift in how farmers view farmlands, both in terms of time and effort. Moreover, farmers are increasingly employing machine vision to distinguish hide patterns and facial features, monitor water and food consumption, record body temperature, and track behavior to keep tabs on the health of their cattle.

Global AI in Agriculture Market Restraint

Lack of Experience with Emerging Technologies

It's essential to recognize the differences between the agricultural sectors of developed countries and emerging ones. Artificial intelligence agriculture could be helpful in some places, but it could be challenging to sell in areas where that kind of thing is still uncommon. Most farmers will require assistance in implementing it. Farmers frequently regard AI as being limited to the digital realm. They may not appreciate how this technology can improve their ability to cultivate the land. It's not because they're old-fashioned or scared of change. Their opposition stems from a lack of knowledge of the practical application of AI tools. However, there is much work to be done by technology vendors to assist farmers in properly implementing AI.

Global AI in Agriculture Market Opportunities

Agricultural Labor Force is Steadily Decreasing

A shortage of qualified workers, aging farmers, and younger generations who find farming unappealing all contribute to the downturn, driving trends toward automated farming operations. As the number of people working in agriculture continues to drop, public and commercial institutions are increasingly investing in artificial intelligence (AI)-based automation solutions to alleviate labor shortages. The developed world is not immune to this downward trend. The agricultural sector in Asia and the Pacific is experiencing a severe labor shortage due to an aging population. Due to the causes mentioned above, the market for artificial intelligence in agriculture is projected to flourish in the following years.

Regional Analysis

The global AI in agriculture market is bifurcated into four regions, namely North America, Europe, Asia-Pacific, and LAMEA.

North America Dominates the Global Market

North America is the most significant shareholder in the global AI in the agriculture market and is expected to grow during the forecast period. Increases in disposable income, ongoing funding for automation, big bets on the Internet of Things, and a growing emphasis from governments on homegrown AI equipment development are all hallmarks of the North American economy. The market also benefits from various agricultural technology vendors researching artificial intelligence solutions. Artificial intelligence (AI) is projected to usher in a technological revolution in the future of farming techniques in the region, with drones, robots, and intelligent monitoring systems being deployed in research and field experiments. Additionally, the regional market is anticipated to be driven by the increasing use of AI-powered technologies in the agricultural sector. In addition, the growing popularity of Internet of Things (IoT) devices in agriculture are anticipated to boost the worldwide AI in the agriculture market in the area.

Asia-Pacific is expected to grow during the forecast period. Rapid expansion is attributed to the expanding use of AI tools in farming. India and China, two of the world's fastest-growing economies, are using artificial intelligence (AI) technologies like remote monitoring and predictive analysis in the food business. Furthermore, the growing demand in these economies for smart cities is prompting agribusiness enterprises to implement AI-powered solutions and services. China is seeing a significant increase in the adoption of AI solutions in agriculture in the region, mainly to Alibaba Group's entry into the agricultural solution market with its AI technology to aid small farmers in the country.

Al is used for row crop cultivation. The robot is so effective at weeding rows of crops that only a twentieth as much herbicide is needed. The European Soil Data Centre is Europe's thematic focus for soil-related data. Its purpose is to act as a central repository for all relevant soil data and information on a European scale. The increasing popularity of Al in agriculture can be attributed to the widespread adoption of AI and computer vision-based monitoring and reporting technologies for indoor and outdoor fields. With over a thousand operating indoor farms in Germany and expansion into other European countries, the demand for Al in agriculture is increasing.

Due to the increasing popularity of AI-powered systems that employ deep learning methods, LAMEA is predicted to have moderate expansion. The global AI in the agriculture market is expected to expand at a staggering rate due to the increasing prevalence of applications that combine IoT and AI, such as predictive analysis, machine learning, and others. Despite the progress made over the previous two decades in agricultural innovation initiatives, there is still a pressing need to fortify agricultural research, technology, and innovation infrastructure to meet these problems.

Report Scope

Report Metric Details
By Component
  1. Hardware
  2. Software
  3. Services
By Technology
  1. Machine Learning & Deep Learning
  2. Predictive Analytics
  3. Computer Vision
By Application
  1. Precision Farming
  2. Drone Analytics
  3. Agriculture Robots
  4. Livestock Monitoring
  5. Others
Company Profiles Microsoft IBM Corporation AgEagle Aerial Systems Inc. Deere & Company Descartes Labs, Inc. Prospera Technologies GAMAYA Taranis ec2ce PrecisionHawk Tule Technologies Inc. Others
Geographies Covered
North America U.S. Canada
Europe U.K. Germany France Spain Italy Russia Nordic Benelux Rest of Europe
APAC China Korea Japan India Australia Taiwan South East Asia Rest of Asia-Pacific
Middle East and Africa UAE Turkey Saudi Arabia South Africa Egypt Nigeria Rest of MEA
LATAM Brazil Mexico Argentina Chile Colombia Rest of LATAM
Report Coverage Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends
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Segmental Analysis

The global AI in agriculture market is segmented based on component, technology, and application.

The market is further segmented by components into hardware, software, and services.

The hardware segment encompasses a variety of robotic equipment, drones, cameras, and sensors employed in AI-powered agricultural applications. This encompasses soil sensors that monitor soil properties, including moisture and nutrients. Uncrewed aerial vehicles (UAVs) or drones for aerial data collection, Autonomous farm equipment and agricultural robotics, and Crop and livestock monitoring cameras with computer vision capabilities. The hardware component facilitates the acquisition of real-time data from the environment, which is subsequently processed and analyzed using AI algorithms. The increasing adoption of agricultural automation and precision farming techniques drives the demand for advanced hardware in the AI in agriculture market.

AI in the agriculture market is dominated by the software segment, the fastest-growing and largest component. It encompasses AI-powered analytics platforms that analyze sensor data and offer insights, machine learning algorithms for predicting crop yields, pest infestations, and other factors, computer vision software for plant and livestock monitoring, and decision support systems that suggest the most effective farming practices. Microsoft, IBM, and Deere & Company are among the most significant participants in this sector, providing AI-powered agricultural software solutions. One of the primary factors driving the adoption of AI software is its capacity to optimize farming operations and automate decision-making.

The market is further segmented by technology into machine learning, deep learning, predictive analytics, and computer vision.

Machine learning and deep learning algorithms are the foundation of numerous AI applications in agriculture. These technologies facilitate the examination of extensive datasets to identify patterns and insights that can be used to inform agricultural decisions. ML models can analyze historical crop yield data, weather patterns, soil conditions, and other factors to predict future yields. Examples of critical use cases include this. By analyzing imagery from drones or ground-based sensors, deep-learning algorithms can detect early indicators of pests or diseases in crops. ML can identify behavioral changes in livestock that may suggest health issues or other issues. Machine learning is a potent instrument for agricultural operations optimization due to its capacity to learn and develop from data continuously.

Predictive analytics employs data mining techniques and statistical models to anticipate future events or outcomes. In the agricultural sector, predictive analytics forecasts near-term weather patterns by analyzing meteorological data, satellite imagery, and other inputs. A more efficient irrigation schedule is achieved by predicting soil moisture levels and plant water requirements. Optimal planting strategies are determined by forecasting market demand, produce yields, and other factors. Rather than relying on historical averages or guesswork, predictive analytics enables producers to make proactive, data-driven decisions.

The market is further segmented by application into precision farming, drone analytics, agriculture robots, livestock monitoring, and others.

Precision farming is the most extensive and rapidly expanding implementation of AI in the agricultural sector. It entails the utilization of cutting-edge technologies, including AI algorithms, sensors, and GPS, to enhance yields and optimize agrarian practices. Predictive analytics for crop yield forecasting and planning, automated guidance systems for farm equipment to reduce overlap and maximize efficiency, and variable rate application of inputs such as fertilizers and pesticides based on soil and crop requirements are among the critical use cases. Precision farming allows farmers to optimize productivity by applying the appropriate quantity of inputs at the proper time and location, thereby minimizing waste and environmental impact.

Drones outfitted with cameras and sensors are utilized more frequently in agriculture to gather aerial data regarding soil and crop conditions. AI-powered analytics platforms can process this data to identify early indications of pests, diseases, or nutrient deficiencies, evaluate crop health and growth stages to optimize management and generate high-resolution maps of fields for precision farming applications. Drone analytics offers producers a bird's-eye perspective on their operations and actionable insights to enhance decision-making.

Market Size By Component

Market Size By Component
  • Hardware
  • Software
  • Services
  • Recent Developments

    • 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.
    • May 2022- AGRA and Microsoft expanded their collaboration to help with the digital agricultural transformation. AGRA and Microsoft signed an MoU in Davos for future collaboration through its Africa Transformation Office. The organizations will leverage their success from a previous partnership started in 2019, which led to the development of the AgriBot.
    • October 2022- Thermo Fisher Scientific Inc., the world's largest provider of scientific instruments and services, has announced a definitive deal to purchase The Binding Site Consortium, a market leader in specialty diagnostics, from an investor group led by Nordic Capital.
    • September 2022- AgroCares, announced a partnership for integrating trinamiX’s high-performance hardware into AgroCares’ next-generation nutrient scanner solution to next-generation solution for onsite nutrient analysis. TrinamiX is built on mobile NIR spectroscopy, combining robust hardware with flexible software to enhance onsite decision-making across various industries in on-the-spot nutrient testing.

    Key Players

    AI in Agriculture Market Share of Key Players

    AI in Agriculture Market Share of Key Players
    Microsoft IBM Corporation AgEagle Aerial Systems Inc. Deere & Company Descartes Labs, Inc. Prospera Technologies GAMAYA Taranis ec2ce PrecisionHawk Tule Technologies Inc. Others Others

    Frequently Asked Questions (FAQs)

    How big is the AI in Agriculture Market?
    The global AI in agriculture market size was valued at USD 1.63 billion in 2023. It is projected to reach USD 7.15 billion by 2032, growing at a CAGR of 20.2% during the forecast period (2024–2032).
    Asia-Pacific region has the highest growth rate in the AI in Agriculture Market.
    Agricultural Labor Force is Steadily Decreasing are one of the key opportunities in AI in Agriculture Market.
    Rising Need for Real-Time Livestock Monitoring are some key drivers supporting the growth of the AI in Agriculture Market.
    The global AI in agriculture market is segmented based on component, technology, and application.

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