AI for Smart Irrigation Using IoT and Weather Forecasting

This research explores the integration of Artificial Intelligence (AI), Internet of Things (IoT), and weather forecasting in smart irrigation systems to optimize water usage in agriculture. Conducted using a descriptive qualitative approach, the study evaluates how AI-based irrigation solutions can mitigate water scarcity, improve crop productivity, and reduce human intervention. The use of sensor-driven data combined with realtime weather predictions allows intelligent systems to determine the right quantity and timing for irrigation. This paper examines the implementation of smart irrigation in developing agricultural economies, where traditional methods often result in overuse of water resources. The technological framework includes machine learning algorithms, moisture sensors, temperature readings, and predictive models that analyze climatic patterns. The findings highlight how AI and IoT together create a sustainable farming ecosystem that supports resource conservation while maintaining yield. Although the adoption of these technologies presents challenges such as high initial costs, limited technical expertise, and data management, their long-term benefits for environmental and economic sustainability are substantial. The research further discusses the socio-economic implications, including changes in labor demand and farmer adaptation. Ultimately, AI-powered smart irrigation stands as a promising solution to modern agricultural challenges through automation, precision, and data-driven decision-making.