The speed ratio parameters of transmission are crucial factors that affect a vehicle’s power, economy, and comfort. Due to the complex working conditions, multiple working modes, and wide range of speed ratios of agricultural tractors, designing and optimizing speed ratio parameters in power-shift transmissions is a challenging task. This paper proposes a transmission speed ratio optimization method based on the life cycle speed utilization rate of general-purpose agricultural tractors. The speed ratio parameters are optimized and solved using the genetic algorithm, with multi-gear power-shift transmissions in agricultural tractors as the research subject. The optimization results and simulation analysis show that the optimized speed ratio has more and denser gears in the common operating speed range while ensuring the general-purpose agricultural tractors’ use requirements. Compared to commonly used geometric series speed ratios, tractors using the optimized speed ratio parameters in this paper can significantly improve fuel economy. Most importantly, this provides a practical method reference for optimizing speed ratios in multi-gear gearboxes with complex structures.

The automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper presents a method for blueberry detection, 3D spatial localization, and pose estimation using visual perception, which can be deployed on an OAK depth camera. Firstly, a blueberry and calyx scar detection dataset is constructed to train the detection network and evaluate its performance. Secondly, the blueberry and calyx scar detection model based on a lightweight YOLOv11 (the eleventh version of You Only Look Once) network with an improved depth-wise separable convolution (DSC) module is designed, and a 3D coordinate system relative to the camera is established to calculate the 3D pose of the blueberry fruits. Finally, the above detection model is deployed using the OAK depth camera, leveraging its depth estimation module and three-axis gyroscope to obtain the 3D coordinates of the blueberry fruits. The experimental results demonstrate that the method proposed in this paper can accurately identify blueberry fruits at various maturity levels, achieving a detection accuracy of 95.8% mAP50-95, a maximum positioning error of 7.2 mm within 0.5 m, and an average 3D pose error of 19.2 degrees (around 10 degrees at the ideal picking angle) while maintaining a detection frame rate of 13.4 FPS (frames per second) on the OAK depth camera, providing effective picking guidance for the mechanical arm of picking robots.

Remote monitoring system for maize seeding parameters based on Android and wireless communication

A combined seedling extraction device was developed that operates by first top loosening and then clamping the stem in order to solve the current issues with automated transplanting technology, such as low seedling extraction efficiency and a high rate of substrate loss. The pepper plug tray seedlings were selected as the experimental subjects for testing the mechanical properties of the stems. The tensile and compressive mechanical properties of the stems were obtained, and the kinematic model of the seedling spacing process and the mechanical model of the seedling clamping process were established. Key parameters of the seedling extraction device were analyzed and calculated, and an automated seedling extraction system was constructed. Using substrate moisture content, seedling age, and extraction frequency as experimental factors, orthogonal tests were conducted. Through variance analysis and 3D response surface analysis, the optimal rounded parameter values were determined: 48% substrate moisture content, 38-day-old seedlings, and a seedling extraction frequency of 60 plants/min. Under these conditions, the seedling extraction success rate was 94.44%, the substrate loss rate was 6.07%, and the seedling damage rate was 4.17%, meeting the requirements for automated seedling extraction.

This article systematically reviews the research progress and challenges in mechanized planting technology and equipment for wine grapes, with a particular focus on the current status and development of the wine grape industry in China. Studies show that the global wine grape cultivation area is extensive, and China, as one of the major producers, has made significant progress in planting scale and technology application in recent years. However, compared to developed countries such as France and the United States, China still lags behind in the full mechanization of wine grape cultivation, especially in winter cold protection and spring soil clearing. This paper provides a detailed analysis of mechanized operations in wine grape cultivation and compares the differences in related technologies and equipment between China and other countries. The study points out that the main problems faced by China in the mechanized production of wine grapes include a wide variety of equipment, complex winter cold protection procedures, diversified planting patterns, and inadequate technical standards. Future development directions should focus on the integration of advanced technologies with traditional equipment, the construction of a full mechanization technology system, the integration of intelligent and information technologies, and the development of multifunctional composite equipment. By addressing these issues, this article provides a theoretical basis and practical recommendations for the full mechanization development of China’s wine grape industry, aiming to enhance its international competitiveness.

In China, most blueberry varieties are characterized by tightly clustered fruits, which pose challenges for achieving precise and non-destructive automated harvesting. This complexity limits the design of robots for this task. Therefore, this paper proposes adding a jetting step during harvesting to separate fruit clusters and increase the operational space for mechanical claws. First, a combined approach of flow field analysis and pressure-sensitive experiments was employed to establish design criteria for the number, diameter, and inclination angle parameters of two types of nozzles: flat tip and round tip. Furthermore, fruit was introduced, and a fluid–structure coupling method was employed to calculate the deformation of fruit stems. Simultaneously, a mechanical analysis was conducted to quantify the relationship between jet characteristics and separation gaps. Simulation and pressure-sensitive experiments show that as the number of holes increases and their diameter decreases, the nozzle’s convergence becomes stronger. The greater the inclination angle of the circular nozzle holes, the more the gas diverges. The analysis of the output characteristics of the working section indicates that the 8-hole 40° round nozzle is the optimal solution. At an air compressor working pressure of 0.5 MPa, force analysis and simulation results both show that it can increase the picking space for the mechanical claw by about 5–7 mm without damaging the blueberries in the jet area. The final field experiments show that the mean distance for Type I (mature fruit) is 5.41 mm, for Type II (red fruit) is 6.42 mm, and for Type III (green fruit) is 5.43 mm. The short and curved stems of the green fruit are less effective, but the minimum distance of 4.71 mm is greater than the claw wall thickness, meeting the design requirements.