Comparing modern and traditional modeling methods for predicting soil moisture in IoT-based irrigation systems

Proper water management is crucial for agriculture, as its irrigation systems can waste approximately 60% of water. Using IoT for irrigation based on soil moisture can help reduce waste. This study used multidimensional time series modeling to predict soil moisture using two years of data on a US farm, including factors such as soil moisture, temperature, and weather. The models assessed were machine learning algorithms like Extreme Gradient Boosting and Random Forests, Deep Learning's Spectral Temporal Graph Neural Network (StemGNN), and Vector Autoregression as a reference model. Random Forest was the most efficient and stable model. Furthermore, the spectral temporal graph neural network could not outperform the reference model. Comparing Random Forest with univariate algorithms like Naive, Exponential Smoothing, and Autoregressive Integrated Moving Average (ARIMA) showed univariate's better performance in 9 of 10 datasets, with ARIMA being best in 8. A substantial disparity is observed when considering the Mean Absolute Percentage Error (MAPE) metric. ARIMA yields an MAPE of 0.052, while StemGNN reports 0.127 as the major difference. ARIMA achieves a MAPE of 0.046 for the minor difference, whereas StemGNN obtains 0.038. In the remaining eight datasets, ARIMA consistently outperforms StemGNN by at least 30%. In summary, modern methods could not beat the older methods in the study data sets.