Our research addresses critical challenges in digital agriculture through interconnected programs that combine technical innovation, socio-technical design, field practice, and governance frameworks.
Four interconnected domains addressing technical and social challenges in equitable digital agricultural systems.
Developing automatic speech recognition systems for low-resource African languages, with focus on agricultural contexts and low-literacy environments.
Building blockchain-based systems that enable transparency and traceability while protecting competitive business data through cryptographic proofs.
Researching technical and policy frameworks for regulatory compliance in agricultural trade, including deforestation regulations and traceability requirements.
Exploring on-chain financial mechanisms that improve access to working capital for smallholder farmers and SMEs while managing risk.
Mixed-methods approaches integrating technical development, participatory field research, and policy engagement.
Practical, production-ready systems designed for resource-constrained environments.
Fine-tuning and quantization of speech models for African languages with limited training data.
Implementing zero-knowledge circuits for supply chain privacy and regulatory compliance.
Designing scalable, secure contracts for tokenization, compliance, and finance.
Optimizing AI models for edge deployment without internet connectivity.
Blockchain-EPCIS patterns for verifiable supply chain traceability.
On-chain mechanisms for working capital access and risk management.
Ongoing research initiatives with transparent progress tracking, research questions, and milestone roadmaps.
We are committed to open science principles. All language models, datasets, and tools developed through our research are released as digital public goods, enabling community-led innovation and building sovereign AI capacity.