Advancements in weather forecasting for precision agriculture: From statistical modeling to transformer-based architectures

Abstract

As precision agriculture (PA) advances, the demand for accurate and high-resolution weather forecasts becomes critical for optimizing agricultural management practices. Despite improvements in Numerical Weather Prediction (NWP) models, they lack the granularity and efficiency needed for PA. Data-driven models offer a promising alternative by integrating predictive capabilities closer to IoT edge data sources, but their efficacy requires evaluation. Here, this paper evaluates six models from three data-driven eras (statistical, machine learning, and deep learning) using agrometeorological data from an Automatic Weather Station (AWS) in Sidi Rahal, East Marrakech, central Morocco, covering 2013–2020 at half-hour intervals, including air temperature, solar radiation, and relative humidity. First, the data is quality-controlled through imputation using ERA5-Land. Then, the dataset was split into training (2013–2019) and evaluation (2020) sets, with validation horizons of 1 day, 3 days, and 1 week. Statistical models generally perform well in air temperature forecasting, occasionally surpassing other models. However, the Temporal Convolutional Neural Network (TCNN) consistently demonstrates superior performance for challenging variables, balancing low RMSE and high R² across various horizons, with some exceptions. Specifically, for relative humidity, the linear regression model achieves slightly lower RMSE (3,96% and 6,05%) compared to TCNN (4,00% and 6,79%) for 1 day and 3 days, respectively. Additionally, CatBoost outperforms TCNN for 1-week forecasts. In terms of training time, the Transformer requires the longest, followed by AutoARIMA and CatBoost. Uncertainty analysis of stochastic models using solar radiation showed the stable performance of TCNN with 0,80 and 0,01 for the RMSE and R² standard deviations, respectively. Considering the trade-off between performance, training time, and capturing complex relationships, TCNN emerges as the optimal choice. ANOVA, Tukey’s HSD and Mann-Whitney U statistical tests also confirmed TCNN’s performance. Finally, a comparison with the Global Forecast System (GFS) reveals TCNN’s clear superiority in all metrics, particularly evident for the RMSE of 3 days air temperature forecasts (TCNN: 1,96 °C, GFS: 3,59 °C).