Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R² values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R² of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery.

Stalk lodging is prone to occur during the harvesting of green forage corn due to head and root anchorage, resulting in loss of harvest. In particular, the fallen stalk wraps around the head, causing a blockage and increasing the energy cost. To address this problem, the deflection model was analyzed and established and a novel method to explore the field bend characteristics of green forage maize was proposed. The effects of stalk diameter, bending angle, and cutting height on bending stress and Young’s modulus were explored by using the method of in-situ measurement. Additionally, the bending deflection, axial displacement and measuring point displacement were obtained. Experimental results indicate that the stalk diameter and bending angle have a significant influence on bending stress. The bending stress has a positive correlation with bending angle and cutting height, while there is a negative correlation with stalk diameter. On the other hand, the bending angle, stalk diameter, and cutting height are closely related to the Young’s modulus. The mean values of the Young’s modulus decrease as a quadratic function with the increasing diameter and bending angle, while the cutting height has the opposite effect on it. Besides, the average values of bending deflection, axial displacement, and measuring point displacement exhibit an increasing trend when the bending angle and cutting height increase. This study results can provide a reference for studying the failure mechanism of green forage maize stalk harvesting, and the design of green forage maize harvesting machinery. Keywords: green forage maize, field measurement, bending stress, Young’s modulus, deflection model, harvest DOI: 10.25165/j.ijabe.20231604.8043 Citation: Fu J, Xue Z, Fu Q K, Chen Z, Ren L Q. Bending properties of green forage maize in field. Int J Agric & Biol Eng, 2023; 16(4): 51–57.

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.

Accurate estimation of corn ear mass is an essential component of intelligent harvesting. To achieve automated mass measurement of large-scale corn ears, this study developed a dual-view prediction platform and proposed a corn ear mass prediction method based on TS-YOLO and Random Forest. First, a lightweight TS-YOLO instance segmentation model was developed using YOLOv11n-seg as the baseline network to extract morphological features. By incorporating the MStarnet multi-scale feature extraction structure, LSKA attention mechanism, and LADH detection head, the proposed model enhanced feature representation of corn ears and damaged regions while reducing model complexity. Compared with the baseline model, TS-YOLO reduced the number of parameters and computational cost by 15.04% and 11.76%, respectively, and achieved an mAP_@.5 of 0.959. Subsequently, a Random Forest regression model was established by integrating morphological features with corn variety and moisture content information. The Random Forest model yielded an R² of 0.894, with corresponding RMSE and MAE values of 18.19 g and 13.97 g, respectively. Across corn varieties and moisture levels, the mean prediction error was 3.86%, indicating consistent and reliable performance under varying sample conditions. The proposed approach enables automated corn ear mass estimation and provides support for intelligent corn ear inspection.