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AI-Powered Disease Detection for Stronger Harvests
Grow Further is backing a breakthrough mobile app in Tanzania that uses artificial intelligence to detect crop diseases early. Developed with the Nelson Mandela African Institution of Science and Technology (NM-AIST), the app helps farmers protect their maize and bean harvests before disease spreads.
The goal: reach 400,000 farmers in five years, 5 million in twenty, boosting yields, income, and climate resilience.
Project Summary
Why Maize and Beans?
Maize is Tanzania’s most important staple crop, and beans are a key protein source. But pests and diseases can wipe out entire harvests. Early detection prevents most losses. This app puts cutting-edge tech into farmers’ hands. It’s simple, fast, and effective.
Project Strategies
Empowering Farmers with Smart Tools for a Changing Climate
Climate Resilience
Climate change increases crop disease risks. This tool helps farmers adapt. Fewer losses mean more stable harvests, less reliance on chemicals, and a stronger food system.
Smart Technology, Simple Tools
The project builds on proven success. More than 67,000 farmers already use a similar banana disease identification app developed by the same team. Now, they are training a new AI model with hundreds of thousands of crop images. The app will be delivered through farmer cooperatives to maximize access, even for farmers without smartphones.
Farmers at the Center
Smallholder farmers are actively shaping the app’s design and functionality to ensure it meets real-world needs. Their input guides design, testing, and rollout to ensure it works in the field and fits their needs.
Better Yields, Stronger Incomes
Timely disease diagnosis leads to faster and more effective interventions, preserving yields and Fast, accurate diagnosis means faster action. Farmers lose less and earn more.
Project Expectations
Grow Further’s Role
Grow Further is funding two years of research and development to build, test, and refine the app. The grant also supports agricultural extension and farmer outreach. As the project expands, local and international partners will lead a wider rollout. Independent evaluation will track impact and scalability.
Project Details
Project Stakeholders and Collaborating Institutions
Grantee Institution
Council for Scientific and Industrial Research-Savanna Agricultural Research Institute (CSIR-SARI), a public scientific research institution in Tamale, Ghana.
Team
- Neema Mduma, Lecturer in Information and Communication Sciences and Engineering, NM-AIST
- Angela Mkindi, Lecturer in Life Sciences and Bioengineering, NM-AIST
- Ritha Ituwe, Accountant, NM-AIST
Key Partners
- Mbeya University of Science and Technology, data collection
- Tanzania Agricultural Research Institute, agricultural expertise
- Rift Valley Cooperative Union, farmer engagement
- Makerere University, machine learning collaboration
