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
Regional Analysis:
Adoption of Generative AI in agriculture can vary widely by region. While developed countries with access to sophisticated technological infrastructure are leading in adopting Generative AI-driven farming practices, developing regions with large agricultural sectors - like Africa and parts of Asia - have also taken steps toward adopting Generative AI technology to increase food security and agricultural productivity.
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
Growth Opportunities:
Generative AI offers farmers tremendous growth potential. This technology can assist with predictive modeling for crop diseases and pests, enabling early intervention with reduced crop losses; optimize irrigation and nutrient management practices in order to save water and fertilizer wastage; as well as assist breeding programs by accurately predicting how different genetic combinations will perform.
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