Feature Engineering and Crop Yield Prediction using Agricultural Time-Series Data

Abstract

The increasing availability of agricultural timeseries data enabled more accurate and data-driven crop yield prediction. However, raw meteorological, soil, and vegetation datasets often fail to capture complex temporal dependencies essential for robust forecasting. This study proposes a structured feature engineering framework for crop yield prediction using multi-source agricultural time-series data, including climatic variables, soil properties, and satellite-derived vegetation indices. Temporal features such as lag variables, rolling statistics, cumulative rainfall indices, and growing degree days (GDD) are systematically extracted to enhance model interpretability and predictive performance. Multiple machine learning (ML) models, including random forest (RF), gradient boosting (GH), support vector regression (SVR), and long short-term memory (LSTM) networks, are evaluated. Experimental results demonstrate that engineered temporal features reduce MAE and RMSE by approximately 25–35% compared to raw feature baselines. The optimized LSTM model achieved an RMSE of 3.96 tons/ha and r 2 of 0.92, outperforming traditional regression models. Feature importance analysis confirms the significant contribution of cumulative rainfall, temperature lags, NDVI (Normalized Difference Vegetation Index) trends, and soil moisture dynamics. The proposed framework provides a scalable and adaptable solution for precision agriculture, enabling improved yield forecasting and data-driven decision-making under climate variability.