Democratizing capital for smallholder sugarcane farmers

 
Smallholder farmers struggle with access to financial services due to lack of credit histories and collateral. This paper presents an AI-driven solution for alternative credit scoring by leveraging satellite imagery, sensor data, weather/climate data, yield history, and market information. An ensemble AI model architecture integrates random forests, regression, neural networks, and time series models to generate robust credit scores and yield predictions, capturing realistic farm productivity and repayment capacity. The modular design enables scalability through transfer learning and open-source tools. Strategic partnerships facilitate digital loan disbursements, automated repayments, and resilience measures like crop insurance. The cost-effective solution promotes financial inclusion, sustainable farming, and data-driven lending decisions. Pilot tests will validate model accuracy and enable over $5 million in loans to 10,000+ farmers, fostering economic empowerment in rural communities.