Experimental study on DEM parameters calibration for organic fertilizer by the particle swarm optimization - backpropagation neural networks.

In order to calibrate the properties of the organic fertilizer particles, this work employs an integrated strategy that combines simulations, machine vision techniques, and physical experiments. Through physical testing, the fundamental physical characteristics of the organic fertilizer particles were identified. The initial analysis was through the Plackett-Burman test.

Study on the interaction characteristics between pot seedling and planter based on hanging cup transplanter.

This study aims to investigate the interaction mechanism between the planter and pot seedlings during the operation of a hanging cup transplanter and establish a nonlinear relationship model between the two. The research employs a coupling method of the Discrete Element Method and Multi-Body Dynamics to construct a discrete element model of the cup-type planter and pot seedlings. By setting different planting frequencies (22, 34, and 46 plants/min) as influencing factors, the mechanical variation patterns and collision motion characteristics of the pot seedlings were analyzed.

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.

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.

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.