Artificial Intelligence in Agriculture Market size was over USD 1.94 billion in 2023 and is projected to exceed USD 30.48 billion by 2036, growing at over 23.6% CAGR during the forecast period i.e., between 2024-2036. In the year 2024, the industry size of artificial intelligence in agriculture is assessed at USD 2.31 billion. The growth of the market can be primarily attributed to the increasing usage of smart sensors in agricultural fields, and the upsurge in demand for agricultural produce around the globe. According to the World Bank, in 2018, agriculture accounted for 4 percent of the global gross domestic product and in some developing countries it accounted for more than 25 percent of the GDP. Along with these, there is a high demand for real-time livestock monitoring globally, which is giving impetus to the application of advanced AI solutions, such as facial recognition for livestock and image classification with body condition score. This in turn is expected to significantly drive market growth in the near future. Furthermore, growing government initiatives towards the adoption of drones for modernizing agricultural practices is projected to offer ample growth opportunities to the market in the near future.
Growth Drivers
Challenges
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Base Year |
2023 |
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Forecast Year |
2024–2036 |
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CAGR |
23.6% |
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Base Year Market Size (2023) |
USD 1.94 billion |
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Forecast Year Market Size (2036) |
USD 30.48 billion |
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Regional Scope |
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The market is segmented by technology into machine learning, computer vision, and predictive analytics, out of which, the machine learning segment is anticipated to hold the largest share in the global artificial intelligence in agriculture market. This can be accounted to the rising adoption of machine learning-enabled solutions by agricultural organizations and farmers around the world for enhancing their farm productivity and gaining a competitive edge in business operations. Additionally, on the basis of offering, the software segment is predicted to acquire the most significant share during the forecast period, which can be credited to the growing usage of AI software to increase farm efficiency, and high integration of mobile technologies with farming techniques. Apart from these, intensifying demand for real-time data management systems is also assessed to boost the growth of the market segment in the imminent time.
Our in-depth analysis of the global market includes the following segments:
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By Technology |
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By Deployment |
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By Offering |
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By Application |
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On the basis of geographical analysis, the global artificial intelligence in agriculture market is segmented into five major regions including North America, Europe, Asia Pacific, Latin America and the Middle East & Africa region. The market in the Asia Pacific industry is anticipated to account for largest revenue share by 2036, propelled by growing adoption rate of AI in agriculture in countries such as India, China, Japan and Australia, and entry of major companies in agricultural solutions business in the region. In addition, growing investments by multinational companies to spread awareness regarding farm analytics and data sciences among farmers is also predicted to boost the region’s market growth in the upcoming years. Moreover, the market in North America is anticipated to gather the largest share during the forecast period owing to the early adoption of technologies such as machine learning and IoT, and rising usage of computer vision for agricultural applications, such as livestock management, precision farming and soil management. In 2021, almost 80 percent of the total number of companies in North America have adopted machine learning technology. More than 4 percent of self-reported data scientists or data researchers in the US specifically work as machine learning engineers.
April 2021- Trimble and HORSCH partnered to enable autonomy in agriculture with the goal of building a future for workflows and autonomous machines in the industry.
January 2019- Microsoft India embarked on a journey to bring AI sensors to agricultural fields with the aim of increasing yields and reducing costs with the help of smartphones.
Author Credits: Nilanjana Ray
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