AI-Driven Wheat Crop Optimization and Yield Prediction Tool

Precise prediction of wheat yield plays a crucial role in food security and resource management in Pakistan. The current research suggests an artificial intelligence-driven framework based on 23 years of agro-meteorological and yield data that predicts wheat production. Several machine learning models were compared, and a two-layer LSTM model performed better because it was able to capture temporal dependencies. The model managed to achieve high accuracy (R2 = 0.979) and low prediction errors, confirming the applicability of deep learning in agricultural forecasting in climate-sensitive regions and its applicability to other staple crops.