Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments.

The rapid and accurate quantitative analysis of cell chemotaxis, which is essential in biology, medicine, and drug development, enables the evaluation of the directional migration capability of cells and the simulation of in vivo cell chemotaxis. However, traditional methods for studying cell chemotaxis often depend on complex experimental procedures, which are not only time-consuming and labor-intensive but also prone to human error. Recently, the rapid advancement of microfluidic technology and deep learning has provided a new way for evaluation of cell chemotaxis. In this study, a chemotaxis evaluation method based on microfluidics and deep learning is proposed. A microfluidic device was designed to simulate cell chemotaxis, allowing for the controlled assessment of cell chemotaxis by generating chemical gradients within microchannels and shear stress. Concurrently, deep learning technology was introduced to identify the migrated and non-migrated states of cell images, thereby enabling the automatic counting and analysis of chemotactic cells. Compared with traditional manual assays, this method not only reduced time and labor costs but also achieved higher accuracy and reproducibility. This innovative approach, which integrates microfluidics and deep learning, provides a novel perspective and tool for cell chemotaxis research. This method not only offers a fresh perspective on cell migration analysis but also has the potential to significantly advance the field of biomedical research, particularly in biosensor development related to drug discovery and disease diagnosis.