Artificial Intelligence-Based Smart Irrigation System for Climate Resilient Agriculture in Benin City, Edo State, Nigeria

Abstract

Climate change has increasingly disrupted agricultural productivity in Benin City, Edo State, Nigeria, manifesting through erratic rainfall patterns, prolonged dry seasons, flooding events, and rising temperatures. These challenges have significantly affected smallholder farmers who rely on traditional irrigation methods that are often inefficient, labor-intensive, and incapable of responding to dynamic environmental conditions. This study proposes an Artificial Intelligence (AI)-based irrigation system as an adaptive strategy to enhance water-use efficiency and promote climate-resilient agriculture in the region. The system integrates Internet of Things (IoT) sensors for real-time monitoring of soil moisture, temperature, and humidity, alongside machine learning algorithms that analyze historical and real-time climate data to predict optimal irrigation schedules. A conceptual framework is developed to demonstrate the interaction between environmental data acquisition, predictive analytics, and automated irrigation control mechanisms. The proposed model aims to minimize water wastage, reduce operational costs, and improve crop yield and quality. Furthermore, the study evaluates the feasibility of implementing such a system within the socio-economic and infrastructural context of Benin City. The findings reveal that AI-driven irrigation systems have strong potential to enhance agricultural sustainability, improve farmers’ adaptive capacity to climate variability, and contribute significantly to food security and rural development in Nigeria and other climate-vulnerable regions.