Weather Forecasting and Crop Yield Prediction Using AI/ML Models

Agriculture is highly dependent on weather conditions, and accurate forecasting is crucial for optimizing crop yield and resource utilization. Traditional weather prediction and crop yield estimation methods often struggle with accuracy due to the complexity of climate patterns and environmental factors. This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) models for weather forecasting and crop yield prediction to enhance agricultural decision-making. The study integrates multiple data sources, including satellite imagery, meteorological data, soil properties, and historical crop production records, to train predictive models. Various AI/ML techniques, such as deep learning, regression models, and time-series forecasting, are evaluated for their effectiveness in predicting climatic variations and crop productivity. The proposed approach aims to improve the precision of weather predictions and yield estimates, enabling farmers and policymakers to make data-driven decisions that enhance food security and sustainable farming practices. Experimental results demonstrate the potential of AI-driven models in providing accurate and real-time insights, reducing the risks associated with extreme weather events and inefficient resource allocation. This research contributes to the advancement of smart agriculture by leveraging AI and ML technologies to develop a robust and scalable framework for predictive analytics in agriculture.