Artificial Intelligence (AI) in Agriculture Market Overview:

The global market for artificial Intelligence in agriculture was worth USD 1,260.8 million in 2021. It is expected to grow at a 24.2% CAGR between 2023 and 2032.

Artificial Intelligence in agriculture can increase yield and productivity. Agribusiness companies adopt artificial intelligence technologies that are predictive analytics-based. AI-based techniques and applications can be used to control pests, increase yield, monitor soil conditions, and improve agricultural-related tasks throughout the food supply chain. Because it analyzes farm data, artificial Intelligence is increasingly used in agriculture to improve harvest quality and accuracy.

Global Artificial Intelligence in Agriculture Market Analysis:

Component Analysis

The market can be divided into software, hardware, and service. A large share of the market is expected to be held by software. AI-based software can improve crop productivity and yield by using prediction-based analytics, as well as computer vision. Predictive analytics-based software is also increasing in popularity, driving growth in the software market. Watson Decision Platform from IBM Corporation and AI Sowing App by Microsoft are two of the most popular predictive analytics-based programs. Deere & Company also manufactures See and Spray pesticide and herbicide distribution systems. These artificial intelligence solutions enable farmers to choose the best time to sow crops, detect crop diseases and monitor crop yield. They also determine the appropriate amount of fertilizers and water required. AI-enabled software in precision farming and drone analytics can drive further growth in AI in Agriculture.

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Application Analysis

The market can be divided into four categories based on their Application: precision farming, drone analytics, and agriculture robots. Precision farming is an AI-enabled application that is growing rapidly in agriculture. It allows farmers to reduce costs and maximize their resources. Over the forecast period, precision farming is expected to hold a large market share.

In precision farming, AI collects, interprets, and analyzes digital data. GPS-equipped combine harvesters use artificial Intelligence to track harvest yields for field variability analysis. This includes differences in soil makeup and water. This analysis and prediction allow farmers to adjust fertilizers or pesticides according to their needs. An AI system can control agriculture robots. These robots can harvest crops efficiently as they can weed and hoe. It combines artificial Intelligence, field sensors, and data analytics. They can be used effectively for many applications. The segment of agriculture robots is driven by the increasing adoption of artificial Intelligence and new developments in robotics.

Technology Analysis

Technology can divide the market into machine learning and deep learning, predictive analytics, and computer vision. Many agribusiness companies use predictive analytics to deploy artificial Intelligence. AgEagle Aerial Systems Inc., Microsoft, and Granular, Inc. have all developed an AI-enabled platform and solutions for agriculture and farming using predictive analytics technology.

Pesticide control, crop disease infestations, irrigation, and drainage management are some of the agriculture industry's major challenges. Farmers can address these challenges using predictive analysis, which uses image analysis and neural networks. To support predictive analytics, drone-enabled agriculture solutions have also been developed. AgEagle Aerial Systems Inc. is a drone-enabled agricultural company that uses artificial intelligence to increase crop yield. It offers drone analytical solutions for the identification and management of crop areas. Predictive analytics is more efficient in agricultural applications, and this segment will see a steady CAGR during the forecast period. Machine learning is being applied to sensor data to transform farm management systems into real artificial intelligence systems. This allows for greater production improvement. Machine learning and deep learning segments are also expected to grow.

Key Market Segments:

By Component

• Service
• Hardware
• Software

By Technology

• Predictive Analytics
• Computer Vision
• Machine Learning & Deep Learning

By Application

• Livestock Monitoring
• Drone Analytics
• Agriculture Robots
• Precision Farming
• Other Applications

Market Dynamics:

According to the UN, the global population will reach 10 billion people by 2050. The need to bring AI into the agriculture industry is driven by a rapidly growing population. A limited supply of arable land and the need to increase food production for food safety drive the need for a green revolution that uses artificial intelligence, the Internet of Things, and big data. AI-enabled apps are used in many areas of the agriculture industry.

In the agricultural industry, artificial intelligence solutions that include drones, robots, and ground-based wireless sensors are being used more frequently. In 2017, Microsoft partnered with the International Crop Research Institute of the Semi-Arid Tropics to create an AI Sowing App. NatureFresh Farms is a U.S. tech company that works on AI technology to analyze plant data at scale and produce accurate yield and harvest forecasts. The artificial intelligence algorithm was used to predict the time it would take for the blossoms to ripen.

Automation in agriculture also helps to allocate resources like fertilizers and water, determine optimal dates for crop sowing, detect weeds, and drive the demand for artificial intelligence solutions. The system can also recommend the number of seeds to be sowed by using long-term weather data, production data and commodity pricing predictions. Many tech companies and start-ups are embracing the multiple benefits of AI-based applications to increase their IoT-enabled devices that can be used for large-scale deployments of AI applications in agriculture.

Regional Analysis:

North America's market accounted for more than 37.9% of the global market in 2021. This is due to its leading industrial automation industry and the adoption of artificial intelligence solutions. North America's strengths include a higher purchasing power, continued investments in automation, significant investments in IoT, and a growing focus by governments on in-house AI equipment manufacturing. Over the forecast period, the Asia Pacific market will show the highest CAGR. This is due to the increasing use of artificial intelligence technology in agriculture. Agribusiness companies are increasingly being encouraged to use AI-leveraging services and solutions because of the growing demand for smart cities. China and India are two emerging economies that have adopted artificial intelligence solutions, such as remote monitoring technology or predictive analysis for the food industry.

Key Regions and Countries covered in thе rероrt:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Russia
  • Spain
  • Rest of Europe
• APAC
  • China
  • Japan
  • South Korea
  • India
  • Rest of Asia-Pacific
• South America
  • Brazil
  • Argentina
  • Rest of South America
• MEA
  • GCC
  • South Africa
  • Israel
  • Rest of MEA

Market Share & Key Players Analysis:

Vendors of artificial intelligence solutions for agriculture are focused on expanding their customer base to gain a competitive advantage in the market. They adopt a variety of strategic initiatives, including acquisitions, mergers, and partnerships. Deere & Company partnered in 2019 with Cultivating New Frontiers in Agriculture, an international agricultural development organization that implements mechanization in agriculture.

Маrkеt Кеу Рlауеrѕ:

• IBM Corporation
• Microsoft
• Deere & Company
• AgEagle Aerial Systems Inc.
• The Climate Corporation
• Granular, Inc.
• Descartes Labs, Inc.
• Prospera Technologies
• aWhere Inc.
• Tule Technologies Inc.
• Other Key Players

For the Artificial Intelligence (AI) in Agriculture Market research study, the following years have been considered to estimate the market size: