In this study, a visual grading system of vegetable grafting machine was developed. The study described key technology of visual grading system of vegetable grafting machine. First, the contrasting experiment was conducted between acquired images under blue background light and natural light conditions, with the blue background light chosen as lighting source. The Visual C++ platform with open-source computer vision library (Open CV) was used for the image processing. Subsequently, maximum frequency of total number of 0-valued pixels was predicted and used to extract the measurements of scion and rootstock stem diameters. Finally, the developed integrated visual grading system was experimented with 100 scions and rootstock seedlings. The results showed that success rate of grading reached up to 98%. This shows that selection and grading of scion and rootstock could be fully automated with this developed visual grading system. Hence, this technology would be greatly helpful for improving the grading accuracy and efficiency.

This study proposes a novel method for the rapid detection of compost moisture content. The effects of the test frequency (1 to 100 kHz), compost moisture content (5% to 35%), temperature (25 to 65 °C), and bulk density (665.6 to 874.3 kg/m³) on the dielectric properties (the dielectric constant ${\epsilon }^{\prime }$ and the loss factor ${\epsilon }^{″}$) in the compost consisting of fresh sheep and manure corn were investigated. The mechanism for the change in dielectric properties was analyzed. The feature variables of dielectric parameters (${\epsilon }^{\prime }$, ${\epsilon }^{″}$, and the combination of ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$) were selected using principal component analysis (PCA), and the selected characteristic variables and the full-frequency variables were used to perform support vector machine regression (SVR) modeling. The results revealed that the increase in both temperature and bulk density in the frequency band from 1 to 100 kHz increased ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$. The PCA–SVR model with both ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$ combined variables achieved the best results, with a prediction set coefficient of determination of 0.9877 and a root mean square error of 0.0026. In conclusion, the method of predicting the moisture content based on the dielectric properties of compost is feasible.

In this study, the influence of multi-pass plasma remelting conditions on quantitative graphite nodules dissolution, surface hardening and wear resistance of 500-12 ductile iron was evaluated. Surface remelting was carried out at various levels of heat inputs using high-temperature plasma beams. The graphite dissolution was uniquely quantified via image analysis approach, and then the remelted surface layer was characterized for phase transformation, microstructure, hardness and wear resistance. The remelting process parameters (arc current and scanning speed) showed significant (p < 0.01) influence, both on graphite nodules dissolution and surface hardening. Consequently, microstructure of the treated layer is modified and the surface exhibited a significant improvement in hardness and wear resistance. In addition, treatment conditions having slower scanning speed showed better surface modification. Therefore, the plasma remelting enhanced surface hardness and dry sliding wear resistance of 500-12 ferrictic ductile cast iron, which makes it suitable for application in a severe wear condition.

One of the primary challenges faced by small rubbing filament machines is the significant variability in material sizes, particularly in the feeding direction. This variability complicates the processing of locally baled straw with a single device. To address this issue, an adjustable feeding and bale-breaking device was developed and tested to enhance the filamentous performance of baled straw. The machine integrates a series of bale-breaking knives along with a pair of feeding and bale-breaking rollers. This paper presents an overview of the machine’s structure and operating principles, alongside an analysis of the forces acting on the straw within the device, which informed the design of key components and devices. A discrete element simulation model suitable for square baled-straw has been established, providing a research foundation for the subsequent optimization of device design parameters. Effects of motor bale-breaking roller rotating speed (x₁), bale-breaking roller height (x₂) and bale-breaking knife quantity (x₃) on the productivity of bonding bond destruction rate (Y₁) and the particle average speed (Y₂) were explored. Three-dimensional quadratic regression orthogonal rotation central combination experiment method combined with response surface method was used to conduct experiments and explore the interaction effects of influence factors on indicators. A regression model of influence factors and evaluation indicators was established through the analysis of variance. The significant factors affecting Y₁ were ordered of x₁, x₂, x₃, and the significant factors affecting Y₂ were ordered of x₂, x₃, x₁. In the interaction of factors, x₁x₂ and x₂x₃ had an extremely significant impact, and x₁x₃ had a significant impact on Y₁; x₁x₂, x₁x₃ and x₂x₃ had a significant impact on Y₂. The optimal structure and working parameters combination were determined to be 1448 rpm for x₁, 268 mm for x₂, and 14 pieces for x₃. Verification experiments demonstrated that the actual values were 96.95% for the straw rubbing rate and 235.13 kg/(kW·h) for the per unit power productivity. The operation of the adjustable feeding and bale-breaking device developed in this study proved effective in enhancing productivity and breaking performance during the feeding of baled straw. It successfully met the design requirements for the grain size necessary for the comprehensive utilization of straw. Overall, this research establishes a foundational basis for the further development of a small, multipurpose straw rubbing filament machine.