As per our research analysis, in 2021, the global Artificial Intelligence in Agriculture Market was valued at US$ 1.52 billion, and by 2027, it is predicted to reach a market capitalization of US$ 5.24 billion. Over the projection period of 2022-2027, it is expected to develop at a high CAGR of 26.35 percent.
The key market growth drivers will be rising globalization and the agricultural industry’s embrace of new modern technology. Rising demand for high-quality agricultural products, supportive government policies and efforts to encourage modern agricultural technologies and practices, and increased industrialization will all exacerbate the market value. Increased research and development spending, as well as an increase in the use of drones on farms, will assist to move the market forward.
An increase in the adoption of cattle face recognition technology is driving the market. Dairy farms can now use advanced metrics like bovine facial recognition systems and picture classification along with body condition score and feeding habits to individually monitor all behavioral variables in a herd of cattle. Drones can be utilized in agriculture for crop field scanning with small multispectral imaging sensors, GPS map production with onboard cameras, heavy payload delivery, and livestock monitoring with thermal-imaging camera-equipped drones, all of which are increasing the industry, are the factors driving the Global Artificial Intelligence in Agriculture Market to carry the sustainable growth and share within the Global Artificial Intelligence in Agriculture Market.
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Segmentation Analysis:
The global Artificial Intelligence in Agriculture Market segmentation include:
By Technology: Machine Learning, Computer Vision, and Predictive Analytics.
During the forecast period, Predictive Analytics is predicted to have the largest share of the global Artificial Intelligence in Agriculture market. The agriculture industry has numerous challenges, including pesticide control, weed management, irrigation and drainage management, weather surveillance, and crop disease infestations. Farmers can use predictive analysis to analyses and manage these challenges using image analysis and neural networks. Predictive analytics will be used to deploy artificial intelligence. For instance, Ag Eagle Aerial Systems Inc., Microsoft, and Granular, Inc. cooperated on a prediction-based analytics technology to develop AI-enabled farming and agribusiness products and platforms.
By Offering: Software, Hardware, AI-as-a-Service, and Services.
During the forecast period, software is predicted to dominate the global artificial intelligence in agriculture market. However, a lack of uniformity in data collecting and sharing is stifling market growth. Machine learning, artificial intelligence, and advanced algorithm design have improved at rapid speed, yet agricultural data collecting has lagged far behind. However, a lack of information and technological skills will hinder the market’s growth. Technological barriers, interoperability concerns, and a lack of standardization will limit the market’s expansion even more. Large-scale technology limits in emerging economies, as well as high costs associated with precision field data collecting, would stifle industry expansion.
By Application: Precision Farming, Drone Analytics.
During the forecast period, Precision Farming is predicted to have the largest share of the global AI in agriculture market. One of the most quickly growing AI-enabled agricultural applications is precision farming. It assists farmers in lowering costs and maximizing resource utilization. Precision farming use artificial intelligence to collect, interpret, and analyses digital data. For example, GPS-enabled combine harvesters use artificial intelligence to track harvest yields and provide georeferenced data in order to analyses field variability, such as variations in water, soil chemistry, or fungus. Based on the information and projections, farmers can tailor fertilizers or pesticides. Agriculture robots with artificial intelligence can perform a variety of activities by combining artificial intelligence, field sensors, and data analytics.
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Regional Analysis:
During the forecast period, Europe is likely to dominate the global artificial intelligence in agriculture market. The European Soil Data Centre (ESDAC) is Europe’s thematic data center for soils, with the objective of serving as a single point of reference for all significant soil data and information at the European level. AI firms run the ‘Internet of the Soil,’ a software and hardware system that monitors soil factors including humidity, temperature, electrical conductivity, and more in European countries.
During the forecast period, North America is predicted to hold the next largest share of the global artificial intelligence in agriculture market. North America is characterized by increased population purchasing power, ongoing automation initiatives, significant IIoT investments, and increased government concentration on in-house AI equipment production as a result of the region’s strong industrial automation industry and adoption of artificial intelligence technologies. Artificial intelligence solutions are being investigated by a number of agricultural technology companies, which benefits the market.
During the forecast period, Asia-Pacific is predicted to have the highest CAGR in the Global Artificial Intelligence in Agriculture Market. The expanding usage of artificial intelligence in agriculture can be attributed to its expansion. Emerging economies like India and China are implementing artificial intelligence technologies like remote monitoring and predictive analysis in the food business. Furthermore, the growing need for smart cities in these economies is encouraging agribusiness businesses to incorporate AI-based products and services.
Latest Industry Developments:
Ninja cart wants to invest in cutting-edge technology to accelerate its ecosystem and will launch an Agri supply chain platform in Q2 to benefit stakeholders. This will help us solidify our position as a one-stop shop for farmers, retailers, small companies, and Kirana (grocery) stores.
Cropin has created an artificial intelligence-enabled platform that forecasts farm performance throughout the world using satellite imaging, weather, and ground data. The model has already been used to forecast a range of agricultural metrics at scale, including crop detection, crop health and stress, irrigation needs, yield, and so on, allowing businesses and farmers to make proactive and predictive decisions based on the data.
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