Agriculture is vital to Ghana’s economy, contributing approximately 20 % to GDP and employing 45 % of the workforce. However, the agricultural sector’s reliance on rain-fed farming, particularly in northern Ghana, exposes it to climate variability, erratic rainfall, and prolonged droughts which lead to chronic food insecurity and economic losses. With only 2 % of farmland irrigated, traditional methods exacerbate water scarcity and low productivity. This study proposes an innovative machine learning (ML) and drone-based precision irrigation system to optimize water use, enhance crop yields, and build climate resilience in northern Ghana. The study deployed internet of things (IoT) soil sensors, weather forecasts, and autonomous drones across 150 smallholder farms in Tamale, Bolgatanga, and Wa through a mixed-methods approach. A random forest ML model predicted irrigation needs, while drones delivered targeted water applications. Results showed a 50.6 % increase in crop yields and a 30–40 % reduction in water usage compared to traditional methods. However, stakeholder interviews and factor analysis identified barriers such as high costs, limited digital literacy, and policy gaps. The study recommends government subsidies, farmer training, and regulatory reforms to facilitate adoption. This scalable model contributes to Sustainable Development Goals (SDGs 2, 6, and 13) and offers a replicable framework for other arid regions in sub-Saharan Africa.
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