Intelligent Crop Yield Forecasting Using Artificial Intelligence and Climate Data

Abstract

The increasing variability in climatic conditions and its significant impact on agricultural productivity have necessitated advanced forecasting systems for accurate crop yield estimation. This study presents an intelligent crop yield forecasting framework that integrates artificial intelligence with climate data to enhance prediction accuracy and support informed agricultural decision-making. The proposed methodology utilizes multi-source datasets, including temperature, rainfall, humidity, solar radiation, and extreme weather indicators, along with historical crop yield records. These data are preprocessed through cleaning, normalization, and feature engineering techniques to ensure consistency and reliability. Advanced machine learning and deep learning models, such as Random Forest, Gradient Boosting, Support Vector Machines, and Long Short-Term Memory networks, are employed to capture complex nonlinear relationships and temporal dependencies between climatic variables and crop yield patterns. The system is designed with real-time data acquisition and adaptive learning capabilities, enabling continuous monitoring and dynamic forecasting under changing environmental conditions. The models are trained and validated using region-specific datasets to ensure robustness across diverse agro-climatic zones and crop types. Experimental results demonstrate that the proposed approach significantly improves forecasting accuracy and reduces prediction errors compared to traditional statistical and empirical models. The framework enables early detection of potential yield fluctuations and supports proactive decision-making for crop planning, irrigation scheduling, and risk management. Additionally, the integration of real-time climate data allows continuous model refinement and adaptive forecasting. The findings highlight the effectiveness of combining artificial intelligence with climate-driven insights for precision agriculture applications. The study concludes that intelligent crop yield forecasting systems provide a scalable, efficient, and data-driven solution for enhancing agricultural resilience, optimizing productivity, and promoting sustainable farming practices.