A Model for predicting tea yield for enhanced food security in Kenya

Abstract

The increasing uncertainty caused by climate change and its effects on crop yields have made it essential to develop accurate predictive models for crop yield in Kenya. By accurately predicting crop yields, stakeholders can effectively plan and manage crop production, ensuring food security and preventing potential food emergencies. This study aims to address this need by utilizing artificial intelligence techniques to develop a predictive model specifically for tea crop yield. The developed model leverages on machine learning algorithms to analyze historical data on tea yield, rainfall, temperature, soil water deficit, and hail damage. These variables are crucial factors influencing tea crop production in Kenya. By training the model with this data, it was able to make predictions about future tea crop yields. The performance and accuracy of the model was evaluated using the Root Mean Squared Error (RMSE) metric, which measures the differences between the predicted and actual values. The outcomes of this research underscore the potential of artificial intelligence techniques in accurately predicting tea crop yield. Leveraging machine learning algorithms and historical data on crucial variables such as tea yield, rainfall, temperature, soil water deficit, and hail damage, the developed model shows promising predictive prowess. This research augments agricultural planning and management practices, bolstering food security and resilience amidst the uncertainties posed by climate change.

Key words: artificial intelligence, climate change, crop yield, food security, Kenya, machine learning, predictive modeling.