DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Agriculture Market by Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026" report has been added to ResearchAndMarkets.com's offering.
The AI in the agriculture market is projected to grow at a CAGR of 25.5% from 2020 to 2026.
The AI in agriculture market growth is propelled by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques. However, the high cost of gathering precise field data restrains the market growth. Developing countries, such as China, Brazil, and India, are likely to provide an opportunity for the players in the AI in agriculture market due to the increasing use of unmanned aerial vehicles/drones by these countries in their agricultural farms.
By technology, the machine learning segment is estimated to account for the largest share of the AI in the agriculture market during the forecast period.
Machine learning-enabled solutions are being significantly adopted by agricultural organizations and farmers worldwide to enhance farm productivity and to gain a competitive edge in business operations. In the coming years, the application of machine learning in various agricultural practices is expected to rise exponentially.
By offering, the AI-as-a-Service segment is projected to register the highest CAGR from 2020 to 2026.
Increasing demand for machine learning tool kits and applications that are available in AI-based services, along with benefits, such as advanced infrastructure at minimal cost, transparency in business operations, and better scalability, is leading to the growth of the AI-as-a-Service segment.
By application, the precision farming segment held the largest market size in 2019.
Precision farming involves the usage of innovative artificial intelligence (AI) technologies, such as machine learning, computer vision, and predictive analytics tools, for increasing agriculture productivity. It comprises a technology-driven analysis of data acquired from the fields for increasing crop productivity. Precision farming helps in managing variations in the field accurately, thus enabling the growth of more crops using fewer resources and at reduced production costs. Precision devices integrated with AI technologies help in collecting farm-related data, thereby helping the farmers make better decisions and increase the productivity of their lands.
Market Dynamics
Drivers
- Increasing Strain on Global Food Supply Owing to Rising Population
- Increasing Implementation of Data Generation Through Sensors and Aerial Images for Crops
- Increasing Crop Productivity Through Deep Learning Technology
- Government Support to Adopt Modern Agricultural Techniques
Restraints
- High Cost of Gathering Precise Field Data
Opportunities
- Developing Countries to Offer Significant Growth Opportunities
- Use of AI Solutions to Manage Small Farms (Less than 5 Hectares)
Challenges
- Lack of Standardization
- Lack of Awareness About AI Among Farmers
- Limited Availability of Historical Data
Companies Profiled
- IBM
- Deere & Company
- Microsoft
- the Climate Corporation
- Farmers Edge
- Granular
- Ageagle
- Descartes Labs
- Prospera
- Taranis
- Awhere
- Gamaya
- Ec2Ce
- Precision Hawk
- Vineview
- Cainthus
- Tule Technologies
- Resson
- Connecterra
- Vision Robotics
- Farmbot
- Harvest Croo
- Peat
- Autonomous Tractor Corporation
- Trace Genomics
- Cropx Technologies
For more information about this report visit https://www.researchandmarkets.com/r/r54x8l
