Abstract

The application of Artificial Intelligence (AI) in agriculture has gained significant attention for its potential to enhance crop yield prediction, offering more accurate and reliable forecasting models compared to traditional methods. This study explores the use of AI-driven techniques, such as machine learning (ML), deep learning (DL), and data mining, to predict crop yields based on a wide range of variables, including weather patterns, soil health, crop management practices, and satellite imagery. By analyzing historical data and real-time environmental factors, AI models can identify complex patterns and relationships that influence crop productivity. This research focuses on developing and evaluating several AI-based models, including Random Forests, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), for predicting crop yields in different agricultural settings. The results demonstrate the ability of AI models to improve the accuracy of yield predictions, reduce uncertainty, and provide more reliable forecasts for decision-making in farming practices. The integration of AI into crop yield prediction is a step towards precision agriculture, allowing farmers to optimize resource allocation, mitigate risks, and enhance food security.