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The global artificial intelligence (AI) in agriculture market was valued at $671.6 million in 2019 and is projected to reach $11,200.1 million in 2030, demonstrating a CAGR of 30.5% during the forecast period (2020–2030). This can be attributed to several factors, including rising demand for agricultural production, increasing adoption of advanced technologies and smart sensors, rapid demand for drones in agricultural farms, and increasing need for livestock monitoring.
Among all regions, North America held the largest share in the AI in agriculture market in 2019. This is due to the higher adoption toward advanced technologies in the agriculture sector for yielding maximum productivity. Certain North American players are offering services to regional consumers by engaging in a partnership with other leading players. Companies like IBM Corporation and Raven Industries Inc. are increasingly collaborating with other players to enhance their offerings for the agricultural industry.
Factors Governing AI in Agriculture Market
Increasing use of smart sensors in agriculture is a major trend observed in the AI in agriculture market. With rise in precision agriculture practices, there has been an increasing use of sensors in the agricultural fields. Through the use of sensor-based technology in agriculture, farmers are now able to map their crop fields accurately, and can also monitor and apply crop treatments only to areas that need it. The rise in development of various operation specific sensors, such as location sensors, optical sensors, electrochemical sensors, mechanical sensors, airflow sensors, soil moisture sensors, and weather sensors, is helping farmers in monitoring and optimizing yields of crops, as well as making them adaptable to changing environmental factors effectively.
Growing demand for monitoring of livestock is one of the prime factors driving the AI in agriculture market. Livestock plays an important role in fulfilling the demand for meat, milk, eggs, and wool. With changing lifestyle, growing population, and increase in per-capita income level, there is a rise in meat, eggs, and milk consumption globally. To fulfill the demand for protein rich foods, health of livestock needs to be monitored on a regular basis.
The advanced applications of AI solutions, such as facial recognition, help to diagnose abnormal cows before they show severe symptoms and reduce additional expenses spent on vaccinations and food. For instance, in China, an AI company is developing a livestock health monitoring system with facial recognition for managing herd of cows. Hence, with increasing use of AI for monitoring the health of livestock, the market for AI in agriculture is growing across the globe.
Segmentation Analysis of AI in Agriculture Market
The machine learning technology category is projected to continue to lead the market throughout the forecast period. Machine learning plays a major role in the AI in agriculture market with a combination of agronomic sciences and data technologies, which is being increasingly adopted by agricultural businesses and farmers across the globe. The rising deployment of information technology (IT) in agricultural applications, such as field conditions management, crop management, and livestock management, is considerably driving the market for machine learning category.
The precision farming category in terms of application generated the largest revenue in the AI in agriculture market, and is expected to continue to lead the market in the coming years. This is due to its growing popularity among farmers as a consequence of increasing need for optimum yield production with limited available resources, resulting in reduce cost for crop production. Furthermore, rapid use of IoT in the agriculture sector is contributing to the growth of the market in the precision farming category.
|Market Size by Segments||Type, Technology, Application|
|Market Size of Geographies||U.S., Canada, Germany, U.K., France, Russia, Italy, The Netherlands, China, Japan, India, Australia, Brazil, Mexico, Argentina, Turkey, Nigeria, South Africa, Saudi Arabia|
|Market Players||International Business Machines (IBM) Corporation, Microsoft Corporation, Bayer AG, Deere & Company, A.A.A Taranis Visual Ltd., AgEagle Aerial Systems Inc., AGCO Corporation, Raven Industries Inc., Ag Leader Technology, Trimble Inc., Google LLC, Gamaya SA, Granular Inc.|
Global Scenario of AI in Agriculture Market
Geographically, APAC is expected to register the fastest growth in the AI in agriculture market during the forecast period. The growth can majorly be attributed to the high adoption of AI in agriculture sector by China, India, Japan, and Australia, and other countries. Within APAC, China is witnessing a huge growth in adoption of AI in agriculture. This is attributed to Alibaba Group’s entry in to the agriculture business with its AI technology to assist farmers in the country.
Competitive Landscape of AI in Agriculture Market
The global AI in agriculture market is fragmented with the presence of numerous players in the market, including Deere & Company, AGCO Corporation, Trimble Inc., Raven industries Inc., and Bayer AG. Some of the other prominent players operating in the market are International Business Machines (IBM) Corporation, Microsoft Corporation, A.A.A Taranis Visual Ltd., AgEagle Aerial Systems Inc., Ag Leader Technology, Google LLC, Gamaya SA, and Granular Inc.
Recent Strategic Developments of Major Market Players
Industry players are consistently focusing on product launches and partnerships to remain competitive in the AI in agriculture market. For instance, in April 2019, Trimble Inc. announced the launch of Farmer Core software subscription that enables farmers in optimizing their farm operations. The Farmer Core software offers AutoSync feature that helps to automatically sync guidance lines, landmarks, boundaries, and operator information using Precision-IQ field application.
Market Size Breakdown by Segment
The AI in agriculture market report offers comprehensive market segmentation analysis along with market estimates for the period 2014–2030.
Based on Type
Based on Technology
Based on Application