As agricultural production continues to increase its requirements for field management， traditional agricultural machinery and equipment have gradually become difficult to meet the production needs of modern smart agriculture. In this context， soil moisture monitoring technology， as a key means of obtaining soil moisture information in modern agricultural management， is playing an important role in promoting the development of agricultural machinery towards intelligence and automation. Therefore， this study thoroughly reviews and analyzes the current research status of soil moisture monitoring technology at home and abroad， focusing on the research progress of three aspects of soil moisture monitoring methods and principles， model construction algorithms， and signal processing methods. Through comparative analysis， the differences and similarities in monitoring methods， principles， model construction algorithms， and signal processing methods at home and abroad， as well as the problems and challenges in practical applications， are summarized. The future development trends of soil moisture monitoring technology in these three aspects are proposed： in terms of soil moisture monitoring methods， a multi-source soil moisture monitoring information platform is constructed to achieve more comprehensive data collection and analysis； In terms of model construction algorithms， machine learning and deep learning algorithms are adopted to customize model algorithm modules for different soil environments and working scenarios， improving the accuracy and applicability of monitoring equipment； In terms of signal processing， the application of multi-source signal fusion technology is strengthened to reduce the impact of the working environment on monitoring equipment.