The application of twin-screw extrusion technology in the field of straw pretreatment is constrained by the closed nature of its structure, and the internal material flow characteristics have yet to be fully elucidated. This has resulted in a paucity of scientific theoretical support for screw configuration design. To address this issue, this study employed the discrete element method (DEM) in conjunction with physical tests to calibrate the simulation model parameters of rice straw powder. The calibration results demonstrate that the discrepancy between the simulation stacking test and the physical test results is 3.68%, thereby indicating that the simulation model parameters are accurate and reliable. Subsequently, an extrusion verification comparison test of rice straw powder was conducted. The results demonstrated that the relative errors between the simulated and actual quality in different regions ranged from 7.95% to 12.45%. This evidence substantiates the applicability and reliability of the established simulation model in simulating the extrusion process of rice straw powder. Furthermore, the variation rules of the parameters of particle motion and their correlation during the extrusion process of rice straw powder were investigated. It was found that the filling degree was significantly correlated with other parameters, and that the screw configuration had a direct influence on the filling degree. Finally, a bench test was conducted to ascertain the viability of the established simulation model in guiding the design of screw configurations. A linear regression equation was derived between the simulated power consumption and the average particle size of extruded samples under different screw configurations. The study offers a particle-scale understanding of the visualization of the extrusion process of rice straw powder and the scientific design of screw configurations, which is of great significance for the industrial application of the extrusion method.

In response to the problems of low efficiency, high labor intensity, and low mechanization in manual tobacco harvesting, a comb-off tobacco picking device for southern hilly tobacco areas was designed following the agronomic requirements and the principle of manual picking of tobacco harvesting in southern China. The device was composed of the power chassis, picking mechanism, and storage mechanism. This study involved the theoretical analysis, structural design, and modeling of the key components, such as chassis structure, combing-type picking mechanism, and power synchronization mechanism. The results of the motion analysis and calculation of the picking process demonstrated that the adjustment range of the comb chain elevation angles was from 12.4° to 20.9°, the comb rod installation distance was 76.2 mm, and the synchronizing mechanism transmission ratio was 3:10. One-factor test and three-factor three-level orthogonal test was performed with the forward speed of the chassis, the distance between the picking device baffles and the elevation angle of the chain with the combing bar as test factors, and the rate of broken and missed tobacco picking as evaluation indicators. It was revealed that the optimal combination of the forward speed of the chassis, the distance between the baffles, and the chain elevation angle were 1.5 km/h, 75 mm, and 12.4°, respectively. Moreover, verification tests suggested that the breakage rate of tobacco leaves was 9.99%, and the probability of missed tobacco picking was 7.31%, both of which satisfy the agriculture requirements and the operational requirements in the agricultural machinery certification syllabus.

The tea plant is a valuable and evergreen crop that is extensively cultivated in China and many other countries. Currently, there is growing research interest in this plant. For the tea industry, it is crucial to develop rapid and non-invasive methods to evaluate tea plants in their natural environment. This article provides a comprehensive overview of non-invasive sensing techniques used for in-situ detection of tea plants. The topics covered include leaf, canopy, and field-level assessments, as well as statistical analysis techniques and characteristics specific to the research. Non-invasive testing technology is primarily used for monitoring and predicting tea pests and diseases, monitoring quality, and nutrients, determining tenderness and grade, identifying tea plant varieties, automatically detecting, and identifying tea buds, monitoring tea plant growth, and extracting tea garden areas through remote sensing. It also helps to evaluate planting suitability, assess disasters, and estimate yields. Additionally, the article examines the challenges and prospects of emerging techniques aimed at resolving the in-situ detection problem for tea plants. It can assist researchers and producers in comprehensively understanding the tea environment, quality characteristics, and growth process, thereby enhancing tea production quality, and fostering tea industry development.