Agricultural mobile platforms provide the physical foundation for sensing, field operations, and material handling in smart farming, yet their design is strongly constrained by crop geometry, terrain conditions, task-specific payloads, energy demand, and operational reliability. This review examines agricultural mobile platforms from four complementary perspectives: design and operational requirements, platform classification, powertrain and mobility technologies, and agricultural applications. Wheeled, tracked, legged and wheel-legged, and rail-guided or constrained-motion platforms are compared in terms of mobility characteristics and suitable operating environments. Power sources, drive systems, steering mechanisms, mobility control, and platform–implement integration are further discussed, with particular attention to electrification, distributed drive, dynamic loads, and coordinated power allocation. Representative applications include crop monitoring and phenotyping, precision crop management, weeding and harvesting, and transportation and multi-purpose operations. Current challenges arise from the strong coupling among terrain adaptability, payload, energy capacity, autonomy, and long-term reliability, as well as limited interoperability between platforms and implements. Future development should emphasize task-oriented reconfigurable platforms, standardized mechanical and electrical interfaces, task-level energy management, and mobility control that accounts for real-time platform and terrain conditions.

This study presents a lightweight RGB vision-based express-parcel detection framework for complex logistics environments. An Express Parcel Recognition (EPR) dataset is first constructed from RGB images acquired through multi-source data collection, followed by unified re-annotation and Active Learning-based pseudo-labeling to reduce manual annotation effort while maintaining label consistency. Based on YOLOv11s, a lightweight detector termed EPR-YOLO is developed by integrating SFAConv and NGAM to strengthen multi-scale feature extraction and global semantic modeling from RGB visual inputs while controlling computational complexity. Quantization-Aware Training is further introduced to obtain an INT8-quantized model with reduced storage requirements and limited accuracy degradation, providing a basis for subsequent lightweight deployment. Comparative experiments, ablation studies, and independent test set evaluations demonstrate that EPR-YOLO achieves an mAP₅₀ of 71.654%, improving the YOLOv11s baseline by 0.854 percentage points while reducing the parameter count by 9.5%; the quantized model further retains most of the detection capability of its floating-point counterpart. In addition, a FastAPI-based Web application is implemented to support RGB image acquisition-based single-image and batch detection, result visualization, and online inference. The proposed framework therefore provides an integrated sensing-to-deployment solution covering RGB image-based parcel perception, dataset construction, lightweight model optimization, quantized deployment, and application-level validation for intelligent logistics systems.