AI-Powered Early Detection of Crop Diseases in Kenyan Smallholder Farms
In 2022, the agriculture sector accounted for 20% of the GDP of Kenya, and it employs 40% of its population. Kenya's 7.5 million smallholder farmers contribute up to 80% of agricultural produce. However, smallholder farmers in Kenya face substantial challenges in maintaining crop health due to a lack of access to timely and accurate crop disease diagnosis. This gap often results in significant crop losses, which not only undermine the livelihoods of these farmers but also contribute to broader issues of reduced food security in the region. Early detection and treatment of crop diseases are critical to minimizing these losses, as timely intervention can prevent the spread of infections and enhance yields. However, achieving this is a formidable task, as many smallholder farmers operate in remote areas with limited access to agricultural extension services, diagnostic tools, and expert guidance. These challenges highlight the urgent need for innovative solutions to bridge the knowledge and resource gap, ensuring farmers can protect their crops and sustain their communities.
The project aims to empower smallholder farmers in Kenya by providing a highly accurate mobile application for crop disease detection, designed with offline capabilities and rapid performance. By increasing disease diagnosis accuracy, reducing crop losses, and improving yields, the initiative seeks to enhance farmers' livelihoods, boost food security, and foster strong partnerships with agricultural extension services.
To minimize significant crop losses and ensure food security, the project aims to design a machine-learning model that recognizes the major commonly grown crops grown in Kenya and the most common diseases they face. The model must maintain high accuracy in order to reflect reliable results in real-time detection and diagnosis. Another core problem is making the detection tool scalable, easy to use, and accessible to bridge the technological gap for farmers.