Across Kenya, Artificial Intelligence is changing the agricultural sector rapidly. A new report by the Global System for Mobile Communications Association points to this shift, indicating that 49% of all AI deployments are on agriculture and food security, zeroing in on data-driven advice to local farmers.
A look at the research from the ‘AI for Africa’ report indicates that predictive AI applications dominate the scene. This could be attributed to the availability of historical datasets, ease of application, and lower computational requirements relative to generative AI models. AI innovation in the agritech sector is at the forefront, and agriculture is an enormous part of the Kenyan economy. For instance, companies like Apollo Agriculture are utilizing AI for agricultural advisory and alternative credit assessment methods.
The Microsoft AI for Good Lab has developed a spatiotemporal machine learning model for detecting malnutrition hotspots, which enables interventions on time with focused assistance and hence reduces the potential damages of malnutrition in vulnerable populations.
AI Applications in Climate Action
The role of AI in agriculture accounts for only 26 per cent share, while that in climate action is at very top. These range from monitoring biodiversity to protection of wildlife, led by large technology companies like AI for Good Lab and non-profits like Rainforest Connection. A critical storage and computing capacity is put at the local level by investments in data centers from tech firms and MNOs that fuel AI momentum.
Infrastructure and Accessibility Challenges
Despite these developments, infrastructural gaps and frequent power outages are some of the major problems. Indeed, all these issues deepen the digital divide, disproportionately affecting low-income groups, the less educated, and rural populations. AI runs the risk of exacerbating existing socio-economic inequalities.
The high cost of hardware, such as Graphic Processing Units and cloud computing, is also a constraint to AI deployment and adoption. For local entrepreneurs and researchers with modest financial resources, the cost of a GPU in Kenya is way beyond reach as it represents 75 percent of GDP per capita and is 31 times more than that in high-income countries.
Skill Development and Education
A huge gap in the skills available undercuts the development of the AI ecosystem. Courses in AI in universities, where they exist, are very far from matching industry needs, and students do not have appropriate practical learning opportunities. There is also a disproportionate emphasis on core AI skills like machine learning and data science and less on multidisciplinary skills needed to really solve pressing socioeconomic challenges.
Innovative Solutions
Deep tech local startup Fastagger is solving some of these with its software infrastructure, developed to run machine learning and AI models directly on edge devices.