Real-time detection of fresh corn ear height can provide a basis for dynamic adjustment of harvester header parameters, reducing mechanical damage and improving harvest quality. This study proposes a corn ear height detection model (CEHD). A YOLO-HAMDF network is developed for ear recognition, in which the core modules—TBDA, GLSA, and AQE—respectively suppress background interference, enhance contextual perception, and optimize bounding-box scoring. Depth information is incorporated to filter non-target regions and improve system robustness. In addition, a DI-DeepSORT module is designed for ear tracking, where DBC-Net and IDA-Kalman, respectively, enhance the discriminability of ReID features and enable independent-dimension adaptive noise modeling with smoothed positional updates. Experimental results demonstrate that the proposed CEHD model achieves a mean absolute error (MAE) of only 3.21 ± 0.05 cm under field conditions, indicating strong stability and practical applicability. In summary, this study presents a stable and reliable corn ear height detection system, achieves real-time monitoring of ear height, and provides data support for the dynamic adjustment of header parameters in fresh corn harvesters.

Alfalfa is a high-quality forage crop whose viscoelastic properties strongly influence the performance of baling, pickup, and stacking operations. In this study, small alfalfa block specimens were tested using a universal testing machine to investigate stress relaxation and creep behaviors under different moisture contents (12%, 15%, 18%), densities (100, 150, 200 kg/m³), and maximum compressive stresses (8, 12, 16 kPa). Experimental data were fitted using viscoelastic models for parameter analysis. Results indicated that the relaxation response consisted of a rapid attenuation followed by a slow stabilization phase. The five-element Maxwell model achieved a higher fitting accuracy (coefficient of determination, R² > 0.997) than the three-element model. The creep process exhibited three stages, including instantaneous elastic deformation, decelerated creep, and steady-state deformation, and it was accurately represented by the five-element Kelvin model (R² > 0.998). Increasing moisture content reduced stiffness, while moderate moisture improved viscosity and shape retention. Higher density enhanced blocks compactness, stiffness, and damping characteristics, resulting in smaller deformation. The viscoelastic response to compressive stress showed moderate enhancement followed by attenuation under overload, with the best recovery and deformation resistance observed at 12 kPa. These findings elucidate the viscoelastic behavior of alfalfa blocks and provide theoretical support and engineering guidance for evaluating bale stability and optimizing pickup–clamping parameters.