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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. [ABSTRACT FROM AUTHOR]

The most important part of fruit cultivation is transplanting, which can be completed efficiently by an automatic transplanter. In this study, an automatic solanum lycopersicum transplanter was developed. It is primarily composed of the following devices: a seedling tray fixator, a mechanical transplanting arm, double horizontal driving modules, double vertical driving cylinders, a seedling separation device, double-crank five-bar planting devices, a power distribution system, and a PLC control system. An experimental test on an automatic transplanter was carried out on dry land. The experimental results showed that when the planting frequency was 80 plants/(min·row), the transplanting success rate was 93.89%, the missed planting rate was 1.58%, the replanting rate was 0.65%, the lodging rate was 1.94%, and the exposed seedling rate was 1.94%. Each device of the automatic transplanter was coordinated to complete the transplanting process. The automatic transplanter met the operation requirements, and it not only transplanted in dry land but also provided a theoretical basis for fruit cultivation in solar greenhouses in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Peatlands are important carbon pools and stable carbon sinks in terrestrial ecosystems. Studying carbon storage and accumulation characteristics can provide a scientific basis for the conservation and restoration of peatlands. Based on the 2014–2015 survey of the Jilin Provincial Forestry Department on the carbon storage of peatlands in the Changbai Mountains, the surveyed 865 peatlands have a total area of 22,900 hm2 and carbon storage of 18,753,300 tons. There are 275 medium‐sized organic carbon (OC) stocks of peatlands (10000–100,000 tons) in this area, and their carbon storage accounts for 41.3% of the total reserves. The peatland carbon in this area is mainly distributed in the Yanbian Korean Autonomous Prefecture, valleys and the lava platform are the most important geomorphological basis for peatland deposits. The Mudanjiang River Basin has the largest carbon storage and it is dominated by a nutrient‐rich peatland. Peatlands in the Changbai Mountains formed and developed since the Holocene, and the carbon accumulation intensity is different in each stage. The average carbon density of peatlands in this area was 69.74 kg/m3 with an average value of 81.77 kg/m2 for OC accumulation per unit area. The average value of the carbon cumulative intensity of peatlands was 164.43 ton/km2 and the carbon accumulation rate of peatlands was 38.96 g/(a m2). [ABSTRACT FROM AUTHOR]