Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation.

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 108-1.72 × 108 Pa, and 5.9 × 106-6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.

YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion.

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields.

Mathematical model for bale-density prediction in large steel roller-type round balers during bale rolling.

To realize the intelligent production of straw bales and improve their economy, the density of straw bales in the working process of large-scale steel roller-type round balers must be measured in real time. Therefore, this study analyzes the forces acting on steel rollers and bales in the bale rolling process, constructs a mathematical model to predict the density of molded bales, and proposes a method for dynamically measuring the density of bales in a round bale machine. Sunflower straw was selected as the test material, and a bale density model validation test was conducted at a test stand. The results showed that the accuracy of the measured bale density of the data acquisition system ranged from 93% to 97%, verifying that the mathematical model for bale density prediction had good accuracy. This study provided an effective strategy for round baler design.

Study on Contact Stress Distribution Characteristics and Damage of Plug Seedlings Based on Flexible Pressure Sensor.

At present, there is a problem that the growth quality is reduced due to damage to the plug seedling pot during the transplanting process. In this study, the pressure distribution measurement system was used to measure the contact area of plug seedlings when they collided with the ground. The effects of seedling age and forward speed on the characteristics of contact stress distribution and potting damage were investigated through a single-factor experiment. The results were comprehensively considered based on the single-factor test, and the Box-Behnken test was used to optimize the design. The matrix loss rate was used as the evaluation index to determine the optimal parameter combination for transplanting: the tray specification was 72, the seedling age was 30 d, and the forward speed was 1.25 km·h-1. This study can provide a reference and technical support for further research on pot damage in plug seedling transplanting. The optimized parameters can provide practical guidance for reducing pot damage and improving growth quality during transplanting plug seedlings.