Western AI Models Struggle in Global South Agriculture

AI models developed and trained primarily on data from Western countries often prove inadequate when applied to agricultural contexts in the Global South. This is because these models fail to account for the unique crop types, environmental conditions, and farming practices prevalent in these regions. Catherine Nakalembe's experience in western Kenya, where she had to collect her own data to train facial recognition technology to identify local crops, exemplifies this challenge. The lack of relevant training data, coupled with issues like high internet costs and limited bandwidth, further hinders the effective deployment of AI solutions in developing nations. To address this, efforts are being made to develop localized AI applications that cater to the specific needs of farmers in the Global South. Digital Green's FarmerChat app, which uses generative AI to answer queries in local languages and diagnose crop issues from uploaded images, is a notable example. By adapting AI to local contexts, it is possible to empower farmers with better information, improve decision-making, and contribute to achieving food security and sustainable development goals. However, it is crucial to ensure that these AI systems are developed and deployed in a way that prioritizes the needs of farmers and avoids exacerbating existing inequalities.