Challenge Area:
Food insecurity in smallholder farming communities.

Problem Statement:
Smallholder farmers struggle to predict crop yields and market demands, leading to food shortages and waste.

Solution Concept:
Create a DSAI-powered agricultural advisory system that integrates satellite imagery, weather data, and market trends to provide personalized crop management recommendations via a mobile app.

Expected Impact:
Improve crop yields by 20% and reduce food waste by 15% for 5,000 farmers.

Next Steps:
Develop image-processing algorithms, build a user-friendly app interface, and test with local farming cooperatives.

Challenge Area:
Food Security

Problem Statement:
Smallholder farmers in developing regions struggle to predict crop yields due to unpredictable weather and soil conditions, leading to food insecurity.

Solution Concept:
Build a crop yield prediction model using satellite imagery and weather data, employing convolutional neural networks (CNNs) for image analysis and time-series forecasting for weather patterns. The PoC includes data quality checks for missing satellite data and modular code for model retraining.

Expected Impact:
Improves yield predictions by 20%, enabling better resource allocation and reducing hunger risk for 100,000+ farmers.

Next Steps:
Incorporate IoT soil sensors, optimize for edge computing, and develop a farmer-friendly mobile interface.