SMART FARMING USING PREDICTIVE AI ANALYTICS

Smart farming has emerged as a transformative approach to modern agriculture by integrating artificial intelligence, Internet of Things (IoT), and predictive analytics to enhance agricultural productivity and sustainability. Traditional farming practices rely heavily on manual monitoring, farmer experience, and environmental assumptions, which often lead to inefficient resource utilization, unpredictable crop yields, and increased operational costs. The rapid growth of agricultural data generated through sensors, satellite imagery, climate monitoring systems, and soil analysis platforms provides new opportunities for intelligent decision-making. However, conventional analytical methods struggle to process large-scale agricultural datasets with nonlinear relationships and temporal dependencies. This study proposes a smart farming framework based on predictive AI analytics that utilizes advanced machine learning models such as XGBoost and Long Short-Term Memory (LSTM) networks to improve agricultural forecasting and resource management. The proposed system collects data from multiple sources including soil moisture sensors, weather stations, crop health monitoring systems, and historical yield databases. Data preprocessing techniques are applied to remove noise and normalize the datasets before model training. XGBoost is employed for crop yield prediction and soil quality assessment due to its strong performance in structured datasets, while LSTM is used for time-series forecasting of environmental conditions such as rainfall and temperature. The system generates predictive insights that assist farmers in irrigation scheduling, fertilizer optimization, and crop planning. Experimental evaluation demonstrates improved prediction accuracy and enhanced decision support compared to traditional machine learning approaches. The results highlight the potential of AI-driven analytics to improve agricultural productivity, reduce resource wastage, and support sustainable farming practices in data-driven agricultural ecosystems