Overview:
Generative AI refers to the application of machine learning algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to produce content or generate data that resembles real-life agricultural situations. Generative AI has found widespread application in agriculture for crop modeling, yield prediction, disease detection, livestock management, and optimizing resource use such as water and fertilizers.
Generative AI in the Agriculture Industry size is expected to be worth around USD 1083.9 Mn by 2032 from USD 125 Mn in 2022, growing at a CAGR of 24.8% during the forecast period from 2023 to 2032.
Key Market Segments
Based on Crop Type
- Wheat
- Rice
- Corn
- Vegetables
- Other Crop Types
Based on Application
- Precision Farming
- Livestock Management
- Crop Management
- Soil Analysis
- Other Applications
Based on Technology
- Deep Learning
- Computer Vision
- Machine Learning
- Natural Language Processing
- Robotics
Based on End-User Industry
- Farmers
- Agriculture Technology Companies
- Agriculture Consultants
- Government Agencies
- Research Institutions
Restraining Factors:
Although Generative AI holds enormous potential, several challenges act as impediments to its widespread adoption in agriculture. These include high costs associated with installing AI systems on small-scale farmers; concerns related to data privacy and security; as well as needing extensive training resources and computational power. Regulatory hurdles or the digital divide may further impede this adoption process.
Top Key Players
- IBM Corp.
- Microsoft Corp.
- John Deere
- The Climate Corporation (a subsidiary of Bayer)
- Ag Leader Technology
- Trimble Inc.
- Prospera Technologies
- Descartes Labs
- Taranis
- Granular (a Corteva Agriscience company)
- Other Key Players
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