Elaine Fredy Kijoti
Detail-oriented data analyst with a strong foundation in data management, business intelligence, systems analysis and design, project evaluation, data collection, report writing, and GIS technologies. Experienced in conducting in-depth data analysis, cleaning, and visualization for data-driven decision-making.
Proficient in data reporting with a keen focus on protecting data privacy and ensuring compliance with data protection regulations.
Interested in data-based research to enhance informative reporting.
Intervention
As climate change intensifies its impact on agriculture and food systems across Africa, open tools and technologies offer transformative potential for adaptation and resilience. This proposal highlights an innovative approach to enhancing food security in Tanzania by integrating artificial intelligence (AI), drone technology, and open tools such as the Nuru App and remote sensing techniques to support cassava production—a staple crop central to household nutrition and economic stability.
Led by Magnoverata Agriculture Farm in Mwanza, this initiative responds to persistent challenges faced in cassava farming, including infestations from cassava mosaic, brown streak disease, and mites. Traditional pest and disease control methods have proven labor-intensive and inefficient across the farm’s 100-hectare plot. While the Nuru App—an open AI tool developed using thousands of cassava leaf images—improved disease detection at the plant level, its use alone proved insufficient in terms of scale and speed.
To address these limitations, the proposed solution integrates AI-powered analysis from the Nuru App with drone technology. Drones equipped with multispectral and RGB imaging sensors autonomously scan cassava fields, collect real-time data, and transmit it for rapid analysis, enabling early detection and response to plant health threats. This open, scalable solution enhances the farm’s climate resilience by reducing crop losses, improving yields, and optimizing pesticide use and irrigation practices.
Additionally, the project embodies principles of climate justice and participatory innovation by involving local youth and agricultural communities in drone operation and data interpretation. It also supports inclusive knowledge systems by promoting the use of open-source tools that are adaptable to local contexts and accessible to smallholder farmers.
This approach exemplifies how open mapping and AI-integrated tools can be harnessed not only for real-time crop surveillance and decision-making but also as a platform for community-driven resilience strategies. As such, this proposal contributes to broader climate adaptation efforts and serves as a replicable model for other regions seeking sustainable, tech-enabled agricultural solutons