Quick Summary
- Data Related Usecases - #Forecast yield, water/nitrogen needs
- #Vision Related - Predict disease, patterns/weather info
- Mix of Skills - Agriscience, Data, AI, Transfer Learning, Customizing to new markets / similar
- Recommendations - Derive insights/spot risks plus options to mitigate / prescriptive options
- Model climate adaptation changing weather to recommend suitable crops
- Impact - 13 countries, 500 crops, 10k varieties
- Interesting - Model to predict a new variety of crop yield
- Intelligent cloud for sustainable agriculture.
- Using Images, Data, Text, historical data everything to build intelligence
Detailed Insights
- Massive dataset / Best position to build knowledge graph
- Apps for Farmers
- Scaling digital solution to 500 crops, 10k varieties
- AI models for 22 commodities, 13 countries
- Provide Data and Infra
- Breadth and Depth in Data from data collected
- Challenging applying model in unseen regions
- General AI capability and Knowledge Graph
- Wheat in India vs Nigeria vs Canada
- Iterating on Capability
- Agriscience, Capability Science
- 13 countries, 2.2 billion hectare scale
- Country scale error predictions Nigeria (Wheat prediction)
- Model predicted disease options
- Modell hypertuned for different farms / conditions / transfer learning
- predict yeild model in crops
- water / nitrogen update
- predict disease / optimize water
- Model an asset
- Sustainability score
- Predict disease - model climate side of risk
- Spread and breadth of dataset
- Domain knowledge Vision, Agriscience
- Models to detect Cloud Detection vs Cloud Shadow
- Model climate adaptation, risks, recommendations
- Predict with minimum dataset
- Model to predict new variety of crop yeild
- Partnering with cloud providers
- Cloud for intelligent agriculture
- Partner with industry and solve problems
- Apps / Platform
Ref - Link
Knowledge graphs can incorporate both structured (for example, coming from a spreadsheet, or precision agriculture equipment) and unstructured data (a twitter feed, images, YouTube video, bulletin board information, books etc.) Knowledge graphs can be successful and valuable if they can uncover new insights by automatically incorporating new data sources, understanding the context, finding new connections, and continuously evolving and learning.
Keep Exploring!!!
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