In response to the practical production challenges posed by the unreliable operation of the V-shaped squeezing soil-covering and -pressing device (VCP) for planters under clay soil conditions in Northeast China, incomplete seed furrow closure, and severe soil adhesion on pressing wheels, this study proposes a device with star-toothed concave discs for soil-covering and -pressing (STCP) with the aim of enhancing the soil-covering quality of planters. The main working principles of STCP were expounded, and its main structural and installation parameters were determined and designed. Based on bionics, with the dung beetle’s protruding head structure as the research object and UHMWPE as the material, an optimized protuberance-type bionic pressing wheel was designed. A Box–Behnken experiment was conducted by taking the width of the compression wheel, the spring deformation, and the installation angle as experimental factors, as well as the weight of the soil adhered to the surface of the pressing wheel (SW) and the soil compactness (SC) as the evaluation indicators. The optimal structural parameters of the pressing device were determined as follows: the width of the pressing wheel was 60.57 mm, the spring deformation was 55.19 mm, and the installation angle was 10.70°. The field comparison tests of soil covering performance showed that the star-tooth concave disc soil-covering device can effectively solve the problem of seed “hanging” and “drying”. The average covered soil weight of the star-tooth concave disc soil-covering device was 241.46 g, and the average covered soil weight of VCP was 223.56 g. Compared with VCP, the average covered soil weight of STCP increased by 8.01%. The variation coefficient of covered soil weight after the operation of the star-tooth concave disc soil-covering device was 3.71%, which was more uniform than VCP. The field comparison tests of soil-covering thickness showed that the uniformity of soil-covering thickness can be significantly improved by adding a star-tooth concave disc soil-covering device to VCP. The comparative tests of soil anti-adhesive showed that the convex hull type pressing wheels optimized by bionics had better soil anti-adhesive performance, and the soil adhesion weight was reduced by 43.68% compared with VCP. The field comparative tests of seedling emergence showed that the seedling emergence rate after STCP operation was 3.9% higher than that of VCP.

Stem mustard, the main raw material for pickled mustard tuber, is widely planted in Chongqing, China, and is an important local cash crop. Under the working conditions of sticky and wet soil in the Chongqing area, conventional furrow seeding has problems such as soil sticking to the furrow opener, poor mulching effect, etc. In this regard, this paper proposes the use of non-contact, soil-based, pneumatic shot seeding, in which seeds are shot into the soil to a predetermined depth by a high-speed air stream. The diameter of stem mustard seeds was found to be 1.33 mm, with a spherical rate of 95.32% using physical and mechanical properties. The high-speed camera test was used to determine the air pressure at the appropriate sowing depth, and the seed entry process was simulated by EDEM 2021 software, which analysed the movement process of the seed after entering the soil, and the structure of the seeder was designed based on the resulting test data. The structural parameters of the shot seeding device were analysed by a hydrodynamic simulation using Fluent 2022 R1 software and the following results were obtained: an outlet pipe diameter D_C of 2 mm, mixing zone length H of 10 mm, mixing zone inlet diameter D of 15 mm, and steady-state gas flow rate of 80 m/s. Simulation seeding verification was conducted on the final determined structural parameters of the seeding device, and the simulation results showed that the seed velocity could reach 32.3 m/s. In actual experiments, it was found that when the vertical velocity of the seeds was greater than or equal to 26.59 m/s, the seeds could be completely and stably seeded into the soil. Therefore, the designed seeding device can meet the conditions of actual seeding experiments. In conclusion, this research offers a practical guideline for the rapid and precise sowing of stem mustard.

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K = 1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper (Zanthoxylum schinifolium) as a specialty economic crop.

Distribution properties of airflow in the full envelope type maize stalk chopping chamber

Accurate determination of soil and contact parameters is crucial for tillage machinery design; however, the interactions among soil, tools, and roots in citrus orchards covered with green manure remain insufficiently defined. This study, therefore, combined physical experiments with DEM simulations to characterize these interactions. Using significance analysis and response surface methodology (RSM), the effects of major factors on angle of repose (AoR) and initial slip angle (ISA) at varying soil depths were evaluated, enabling precise calibration of both external (soil–machinery) and internal (particle–particle) parameters. Subsequently, a GA-BP optimization model was constructed to enhance calibration accuracy, yielding optimal values for the soil-to-soil rolling friction coefficient (γ = 0.125–0.136), soil-to-65Mn static friction coefficient (μ′ = 0.431 − 0.540), and soil surface energy (JKR = 0.952 − 1.091 J·m⁻²). Shear tests using the bonding V2 model were conducted to calibrate the Bonding parameters of green manure stems and roots, while pull-out tests and simulations were used to validate the root–soil parameters. Direct shear tests confirmed the model’s reliability, with errors in internal friction angle and cohesion below 10%. These findings may contribute to improving DEM simulation accuracy for soil improvement under green manure coverage and support the optimization of soil tillage in citrus orchards.