Hybrid Deep Learning Framework for Crop Yield Prediction and Weather Impact Analysis

Agricultural productivity is seasonal and reliant on climatic variability and soil conditions. Accurate predictions of crop yield are necessary for food security, predicting resource allocation, and planning for climate-resilient farming. The study introduces a hybrid machine learning framework with Random Forest Regression (RFR) for feature selection and Long ShortTerm Memory (RNN-LSTM) networks when weather conditions are variable to predict crop yield. Using simulated datasets, (weather, soil and crop management features) the paper proposes a new methodology formulated mathematically and with algorithms, equations, tables, and flow diagrams. The results indicate, the hybrid model performed better than the baseline regression and deep learning models as measured by RMSE, MAE and R². Sensitivity analysis indicated that rainfall and temperature were the most impactful weather factors for crop yield. The paper ends with implications for precision agriculture and future research work.