Global Artificial Intelligence in Agriculture Market 2021

Market Overview

The global artificial intelligence in agriculture market is experiencing substantial growth, with a valuation of $1,517.0 million in 2022 and an expected reach of $4,096.1 million by 2027. This growth can be attributed to the increasing adoption of digital and smart agriculture equipment and technologies, driven by the rising demand for food and grains.

The implementation of smart farming technologies and solutions has led to the generation of complex data in the agriculture sector. Farmers encounter various variables that they can control, such as variety selection, seeding time, and fertigation rate, as well as uncontrollable environmental factors. Artificial intelligence in agriculture plays a crucial role in dealing with multifactorial problems and finding correlations between inputs and outputs despite high variance and randomness.

Product Type

Market Research



Release Date

March 2023

Base Year


Forecast Period


Market Size

USD 1.3 billion in 2020

Market Segments

Application, Mode of Deployment, Product Type, Region



No. of Companies Mentioned


Artificial intelligence encompasses advanced objects integrated with technology that enable operations like sensing, processing, and communication with other networks. Governments worldwide have shown increased attention to AI in agriculture, leading to significant developments and research programs. For example, the U.S. Federal Government allocated a $973 million budget for non-defense AI-related technology and innovation, including agriculture, in 2019. Regulatory barriers have also been removed to facilitate secure development and testing of AI technologies, fostering the creation of new AI-based enterprises and the adoption of AI by existing ventures. Additionally, a specific portion of the federal budget for R&D is dedicated to artificial intelligence.

The integration of technology with farmers’ skills aims to enhance the quality and quantity of agricultural commodities. Technological advancements have introduced sensors, drones, IoT, and robotics, streamlining farming activities such as planting, growing, harvesting, and monitoring.

Furthermore, artificial intelligence is gaining traction in aquaculture and livestock farming. Market leaders and investors are making substantial investments and adopting business strategies in the artificial intelligence in agriculture market. In the aquaculture industry, AI is playing an expanding role, addressing the increasing global demand for seafood and providing greater efficiencies and insights into fish farming.

Market Segmentation

The market is segmented based on various factors, including application, mode of deployment, product type, and region.

Segmentation by Application
Yield Optimization
Aquaculture Management
Livestock Monitoring
Data Analytics

Segmentation by Mode of Deployment
On-Cloud Deployment
On-Premises Deployment
Edge Deployment
Hybrid Deployment

Segmentation by Product Type
Application Programming Interface (API)
User Interface (UI)

Segmentation by Region
North America
Asia Pacific
Middle East & Africa
South America

Agricultural technologies are available for a wide range of applications in agriculture, covering every stage of the farming cycle. These technologies include data management, soil management, yield mapping, monitoring, spraying, harvesting, and planting, among others. The adoption of agriculture technologies worldwide has led to the accumulation of a vast amount of agricultural data related to on-farm operations and fields. Managing this big data has become a pressing need to improve accuracy, productivity, and reduce manual data entry. Consequently, the introduction of artificial intelligence (AI) in the agriculture sector has been well received by growers and other customers, as AI can optimize agricultural operations based on this data and has found innovative applications.

One of the most deployed uses of AI in agriculture is data analytics, which helps enhance farm production efficiency and reduce losses. By leveraging cutting-edge technologies like AI, the manufacturing industry has also become increasingly reliant on data-driven processes to mitigate supply chain risks. For example, the use of AI trained with deep reinforcement learning (DRL) methods enables manufacturers to optimize various production processes, such as extruders, robotics, and chemical processes. Computer vision technology in controlled environment agriculture (CEA) allows growers to anticipate yields, detect issues in advance, and optimize resources to increase output.

Cloud AI deployment software refers to the use of AI services and platforms to process data and provide insights for decision-making. This strategy allows businesses to take advantage of the scalability of the cloud and easily access AI services from anywhere with an internet connection. It involves deploying AI models on local hardware within an organization’s own facilities, rather than relying on distant servers. An example of AI deployment software in agriculture is the use of computer vision algorithms to recognize and categorize crops or pests. Local hardware, such as cameras or sensors, would collect data, and the AI model would be installed on a local server or computing devices.

Cloud AI deployment software can be applied in agriculture to maximize crop yields, minimize waste, and promote sustainability. For instance, farms can use this software to gather information from sensors placed throughout their fields, such as temperature, humidity, and soil moisture. The collected data is then processed using cloud-based AI algorithms to predict yield and quality and provide insights into crop health.

Companies can make their program data and functionality available to external developers, business partners, and internal divisions through application programming interfaces (APIs). APIs enable different services and products to interact and utilize each other’s data and capabilities. User interface (UI) refers to the point of interaction and communication between humans and a device or software. By leveraging AI models to acquire agricultural data and present them to farmers, researchers in the field of human-computer interaction (HCI) aim to create interactive computer interfaces that meet users’ needs.

The global artificial intelligence in agriculture market is projected to experience significant growth from 2022 to 2027, particularly in North America, Europe, Asia-Pacific, and other regions. This growth is mainly attributed to the increased integration of technology in the agricultural sector and government initiatives to support it. Supportive government policies and economies of scale operations have led to farm consolidation becoming common, especially in developing countries.

For example, the U.A.E. government announced the Agriculture Innovation Mission for Climate in April 2021, a joint initiative with the U.S. The mission aims to accelerate innovation in agriculture and research and development activities related to climate-smart agriculture and food systems modernization. In North America, there are various initiatives to expand the use of AI in agriculture. AI applications are being developed to assist ranch, farm, and forest managers in decision-making. The use of data generated from sensors and aerial images for crops is also contributing to the growth of the AI in agriculture market. Additionally, robotics, automation, and AI-driven decision support systems have the potential to revolutionize agriculture in North America. Initiatives like Agroview are being developed to help farmers improve their economic and environmental sustainability, highlighting the growing interest in AI adoption and innovation in the agricultural sector across North America.

Competitive Landscape

The global artificial intelligence in agriculture industry is characterized by fragmentation, with agricultural companies taking the lead through acquisitions or collaborations with technology-based firms to enhance their solutions and stay competitive in the rapidly growing IoT-integrated agriculture market. In August 2021, for example, CropX Inc. acquired Dacom Farm Intelligence as part of its strategy to expand into Europe. This move allowed CropX to consolidate its agriculture digital twin capabilities, geographic datasets, and serviced acres.

Several manufacturing companies with a global presence have emerged as key players in this industry, including Microsoft Corporation, Climate LLC, CNH Industrial N.V., Deere & Company, and IBM Corporation. Together, these companies hold around 42.90% of the total global artificial intelligence in agriculture market. The remaining 57.10% of the market share is dominated by Aquabyte, Ceres Imaging, Taranis, and CropIn Technology Solutions Pvt Ltd., among others.

Key Companies Profiled in this report include Alibaba Group Holding Limited, Aquabyte, Ceres Imaging, Climate LLC, CNH Industrial N.V., Connecterra B.V., CropIn Technology Solutions, Deere & Company, DJI, Granular Inc., IBM Corporation, Microsoft Corporation, PrecisionHawk, Inc., Plantix, XPERTSEA, Agrible, Prospera Technologies, Taranis, UMITRON, Wolkus Technology Solutions Private Limited (Fasal).

Recent Developments

In May 2021, Robert Bosch GmbH introduced its new Internet of Things (IoT) platform based on Artificial Intelligence (AI) technology. This platform enables real-time monitoring of energy consumption and electrical parameters in various sectors such as healthcare, agriculture, and others.
In February 2019, Ceres Imaging developed and launched the Center-Pivot Analytics Suite, a comprehensive imagery solution specifically designed for center-pivot irrigated row crops.
In February 2022, Plantix expanded its market presence in the Asia-Pacific region by launching the Plantix Vision API in Bangladesh.
In February 2021, Microsoft Corporation initiated its business expansion in Indonesia to provide support to the agricultural sector in the country.
In May 2021, Valmont Industries acquired Prospera Industries for $300 million with the aim of delivering increased benefits to mutual customer farmers.
In February 2021, DeHaat acquired Farm Guide, a data science start-up, to enhance its advisory and market services.
In December 2021, Connecterra B.V. collaborated with Lely to leverage Connecterra B.V.’s advancements in machine learning and AI. This collaboration aimed to provide farmers with greater insights and integrate farm technologies with AI-enabled sensors for the development of farm management systems.
In May 2022, Taranis expanded its partnership with DJI and in the United States. This collaboration leverages cutting-edge AI and drone technologies to enable more informed data-driven decision-making.

Key Questions Answered

What is the projected revenue size of the global artificial intelligence in agriculture market for the forecast period 2022-2027, and what is the expected compound annual growth rate (CAGR) during this period?
What are the key trends, market drivers, and opportunities in the artificial intelligence in agriculture market?
What are the main factors restricting the growth of the global artificial intelligence in agriculture market?
What new strategies are existing market players adopting to expand their market position in the industry?
What is the competitive strength of the key players in the artificial intelligence in agriculture market based on their recent developments, product offerings, and regional presence?
How do the key artificial intelligence in agriculture companies compare in terms of market coverage and potential?
What revenue and growth percentage are expected in each segment during the forecast period, including product (application programming interface and user interface), mode of deployment (on-cloud deployment, on-premises deployment, edge deployment, and hybrid deployment), and application (yield optimization, data analytics, livestock monitoring, and aquaculture management), as well as region (North America, the U.K., Europe, Asia-Pacific, China, the Middle East and Africa, and South America)?
Who are the players and stakeholders operating in the artificial intelligence in agriculture market ecosystem, and what is their significance in the global market?
Which consortiums and associations are leading in the global artificial intelligence in agriculture market, and what are their roles? How does the regulatory landscape vary across different regions?
How are emerging technologies such as the Internet of Things (IoT), 5G communication, and blockchain driving the growth of the artificial intelligence in agriculture market?
Which major patents have been filed in this space?
How is government involvement in environmental issues and safety shaping the artificial intelligence in agriculture industry?
What has been the impact of the Ukraine-Russia crisis on the artificial intelligence in agriculture market?

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Global Artificial Intelligence in Agriculture Market 2021

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