The field of precision agriculture is moving towards increased automation and data-driven decision making. Recent developments have focused on leveraging Internet of Things (IoT) devices, machine learning, and remote sensing technologies to improve farming efficiency and productivity. Automated data collection and analysis are becoming essential tools for farmers, enabling them to make informed decisions about crop management, soil health, and resource allocation. Noteworthy papers in this area include the development of a low-cost GNSS IoT solution for precision agriculture management, which demonstrated the potential for automated work records and improved farm management decision making. Another significant contribution is the design and evaluation of a UGV-based robotic platform for precision soil moisture remote sensing, which showed promising results for real-time data acquisition and reduced labor costs. Additionally, the use of computer vision systems to monitor furrow quality in real-time and the application of machine learning algorithms to map weed management methods in orchards are also notable advancements in the field.