Crop Yield Forecasting with LSTM Models for Precision Agriculture

Learn to build a crop yield forecasting system in this 25-minute tutorial focused on implementing Long Short-Term Memory (LSTM) models for precision farming. Explore data cleaning techniques using a Canadian government dataset, visualize farm locations with Google Maps, and develop a complete machine learning pipeline. Master essential steps including data preprocessing, feature exploration, LSTM model architecture, and training procedures using Python, TensorFlow, and Scikit-learn. Create an interactive system that predicts future crop yields based on historical data and user-selected farm locations. Gain hands-on experience with time-series forecasting, data visualization, and practical applications of AI in agriculture while working with real-world datasets. Perfect for data scientists, agricultural professionals, and AI enthusiasts looking to apply predictive modeling in farming applications.