The global AI in agriculture market is growing due to several factors such as advancements in AI and data analytics, government support and initiatives, and increase in adoption of digital farming solutions. 

NEW CASTLE, Del., Aug. 8, 2024 /PRNewswire/ -- Allied Market Research published a report, titled, "AI in Agriculture Market by Component (Solution, and Services), Technology (Machine Learning, Computer Vision, and Predictive Analysis), and Application (Soil Monitoring, Livestock Health Monitoring, Intelligent Spraying, Drone Analytics, and Others): Global Opportunity Analysis and Industry Forecast, 2024-2032". According to the report, the artificial intelligence (AI) in agriculture market was valued at $2.3 billion in 2023, and is estimated to reach $14.6 billion by 2032, growing at a CAGR of 23.2% from 2024 to 2032.

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Prime determinants of growth 

The global AI in agriculture market is growing due to several factors such as advancements in AI and data analytics, government support and initiatives, and increase in adoption of digital farming solutions. However, high cost of initial development restrains the development of the market. In addition, surge in demand for precision farming solutions is expected to provide ample opportunities for the market growth during the forecast period. 

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Report coverage & details: 

Report Coverage 

Details 

Forecast Period 

2024–2032 

Base Year 

2023

Market Size in 2023 

$2.3 Billion 

Market Size in 2032 

$14.6 Billion 

CAGR 

23.2 %

No. of Pages in Report 

488

Segments Covered 

Component, Technology, Application, and Region 

Drivers 

  • Advancements in AI and data analytics 
  • Government support and initiatives 
  • Increase in adoption of digital farming solutions 

 

Opportunities 

  • Surge in demand for precision farming solutions 

 

Restraint 

  • High cost of initial development

 

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The solution segment is expected to grow faster during the forecast period.

By component, the solution segment held the highest market share in 2023, owing to rise in demand for smart agricultural solutions and platforms powered by AI, IoT, and big data analytics. These solutions integrate data from various sources such as sensors, drones, and satellite imagery to provide farmers with insights and recommendations for crop management, pest control, and resource optimization. 

However, the services segment is projected to attain the fastest growing segment from 2023 to 2032. This is attributed to the fact that service providers offer expertise in tailoring AI solutions to meet specific requirements, integrating them seamlessly into farm operations, and providing ongoing support and maintenance. 

The predictive analysis segment is expected to grow faster during the forecast period.

By technology, the predictive analysis segment held the highest market share in 2023. This is attributed to the fact that predictive analysis enables farmers to anticipate future outcomes based on historical data, current conditions, and predictive modeling. 

However, the machine learning segment is projected to attain the fastest growing segment from 2023 to 2032. Machine learning algorithms excel at processing and analyzing large volumes of agricultural data collected from various sources, including sensors, drones, satellites, and farm equipment 

The drone analytics segment is expected to lead during the forecast period.

By application, drone analytics segment held the highest market share in 2023. Integrating drone analytics and AI in agriculture offers tremendous potential for optimizing agricultural operations, reducing costs, and enhancing sustainability. By leveraging the power of AI to analyze drone-captured data, farmers can make data-driven decisions, improve resource allocation, and achieve higher productivity. Therefore, drone analytics is expected to be a significant driver of the AI in agriculture market. 

However, the precision farming segment is projected to attain the fastest growing segment from 2023 to 2032. Precision farming techniques, enabled by advanced technologies such as AI, IoT, and data analytics, offer farmers the ability to optimize resource usage, reduce waste, and increase productivity. 

North America to maintain its dominance by 2032

Region-wise, North America held the highest market share in terms of revenue in 2023 and is expected to dominate in terms of revenue during the forecast period. With rise in adoption of AI technologies in the agricultural sector, the market is expected to expand significantly in the coming years. Factors such as the need for increased productivity, rise in demand for precision farming techniques, and the availability of advanced infrastructure contribute to the market growth. 

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Major Industry Players: - 

  • Microsoft Corporation 
  • IBM Corporation 
  • Gamaya 
  • Taranis 
  • PrecisionHawk 
  • Corteva 
  • ec2ce 
  • Valmont Industries 
  • Climate LLC. 
  • Prospera Technologies, Ltd. 

The report provides a detailed analysis of these key players in the global AI in agriculture market. These players have adopted different strategies such as new product launches, collaborations, expansion, joint ventures, agreements, and others to increase their market share and maintain dominant shares in different regions. The report is valuable in highlighting business performance, operating segments, product portfolio, and strategic moves of market players to showcase the competitive scenario. 

Recent Industry Development: 

  • In April 2024, AGRIST launched a farm robot namely L, to address the issue of a labor shortage in agricultural harvesting. It is able to run continuously for four hours on a single battery charge. L travels along a wire that is put inside the greenhouse, in contrast to the majority of other robots and AI solutions in the industry that operate on the ground. 
  • In February 2024, Heifer International and FruitPunch AI launched the AI for Women Farmers Challenge, which is a project to develop AI tools to help rural women farmers in Nepal leverage data to improve their livelihoods. The project aims to help women-run agricultural cooperatives extract and translate their own data on financial, business, and other activities, which the co-op members can then use to improve their access to markets, financing, product traceability platforms and more. 

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