AI-Powered Precision Agriculture for Sustainable Yield and Resource Efficiency in African Farming

Abstract

This research examines the transformative potential of AI-driven precision agriculture technologies, including autonomous drones, AI-powered sensors, and advanced machine learning analytics, in enhancing agricultural productivity, resource efficiency, and sustainability among smallholder farmers in Africa. Employing predictive models based on sophisticated machine learning algorithms such as ARIMA, Random Forest, XGBoost, and LSTM, the study forecasts significant yield enhancements, improved market price predictions, and notable resource savings in water, fertiliser, and energy usage from 2022 to 2030. The findings demonstrate considerable improvements, including increased yield accuracy, optimised resource utilisation, and heightened economic viability compared to traditional farming methods. Moreover, the study identifies key barriers and opportunities that influence technology adoption, suggesting that strategic investments and targeted policy interventions are essential components for successfully scaling these innovations. Ultimately, this research provides critical insights and practical recommendations to drive sustainable agricultural development and economic empowerment across African agrarian communities.