Developing effective soil moisture estimating systems can provide substantial information for various applications including precision agriculture and ecosystem management. This highlights the need to use data mining and machine learning algorithms to estimate soil moisture accurately. For this purpose, an efficient backtracking search-based extreme gradient boosting algorithm (BS-XGB) algorithm is presented for soil moisture estimation. The incentive mechanism of the proposed BS-XGB is tuning the hyper-parameters of the extreme gradient boosting optimally by incorporating the backtracking search algorithm, which significantly improves the prediction performance. The proposed algorithm is evaluated on a benchmark dataset containing daily soil moisture parameters in four depths 10, 25, 50, and 100 cm sampled from the Kingston station in the United States of America. The results indicated that the BS-XGB model achieved impressive performance in estimating soil moisture, with an R² of 0.999 for the training dataset and an R² of 0.973 for the testing dataset. Comparing the results of BS-XGB with those of the counterpart algorithms proved its superiority in terms of statistical metrics. The feature importance analysis suggested that the variables of soil temperature, relative humidity, minimum temperature, and solar radiation are the most important factors in soil moisture prediction. The results reveal that the proposed model with a high degree of confidence can be used as a qualified alternative to predict soil moisture and save time and cost.
