Analysis and Survey of Soil Moisture Prediction Techniques for Agricultural Applications

Soil Moisture (SM) is an important factor disturbing the growth of crop. Thus, sophisticated understanding or precise prediction of future SM states are important in scheduling of irrigation, improving utilization of agricultural water, and forecasting of yield. Hence, the detection and prediction of SM are of major concern in the present era. This review article provides the detailed review of latest research papers presenting the SM prediction approaches for the prediction of SM. The papers are classified as conventional methods of SM prediction, Remote sensing based SM prediction approaches, Machine learning based SM prediction methods, and the deep learning based SM prediction approaches. In addition to this, various research gaps and the challenges associated with the existing works of SM prediction are discussed. The reviewed works are analyzed in the basis of performance metrics, performance attained using various methods, and the datasets employed for analysis. In addition, this review presents the future scope for the researchers with the analysis of issues associated with the existing literary works.