AI-enhanced climate adaptation strategies for smallholder farmers in a warming world

This research investigates the potential of AI-enhanced climate adaptation strategies for smallholder farmers in the context of global warming. Climate change is increasing in intensity and smallholder farmers are struggling as they face price volatility, unemployment, crop failure and increasingly scarce natural resources in many places. It investigates the use of “support vector machines (SVM), random forests (RF), artificial neural networks (ANN) and k nearest neighbor (KNN) type of AI algorithms for enhancing farming practices and for making them more resilient to climate change.” These algorithms were evaluated in terms of how well they could predict crop yields, optimize irrigation, and detect pests by using data from real time environmental sensors, weather forecasts, and crop performance indicators. ANN performed the best as it had a yield prediction accuracy of 92.4%, followed by RF, SVM with 89.7%, 85.6% respectively and then KNN with 82.1%. AI integration with IoT devices for actionable insights offered the farmers a chance to make informed decisions, resulting in water use efficiency improved by 15% and crop yields by 10%. The use of AI as a tool to support sustainable farming and climate resilience for smallholder farmers, and particularly for small holder farmers living in low income countries is demonstrated.