The transformation and upgrading of traditional agriculture are required to address its shortcomings and deficiencies, which have resulted in environmental pollution or water problems. Precision agriculture emerged at the historic moment to solve the current problems. Field information collection, information management and decision-making, and execution systems are the three key links of precision fertilization and irrigation. The technical principle and application of field information acquisition systems are analyzed. The information management and decision-making system describes the management and summary of information in crop growth. The execution system combines the knowledge of various disciplines and experts for targeted applications to specific crops. It further focuses on the core implementation system, that is, variable fertilization technology and variable spraying technology that can realize variable operations. Major contributions from different countries, institutions, corresponding authors, and journals are presented in detail. This study proposes several suggestions and ideas based on the research status and progress of the three key systems to provide a theoretical basis and technical support for the research and development of key technologies and innovative devices of precision agricultural fertilization and irrigation.

Domestic trash detection is an essential technology toward achieving a smart city. Due to the complexity and variability of urban trash scenarios, the existing trash detection algorithms suffer from low detection rates and high false positives, as well as the general problem of slow speed in industrial applications. This paper proposes an i-YOLOX model for domestic trash detection based on deep learning algorithms. First, a large number of real-life trash images are collected into a new trash image dataset. Second, the lightweight operator involution is incorporated into the feature extraction structure of the algorithm, which allows the feature extraction layer to establish long-distance feature relationships and adaptively extract channel features. In addition, the ability of the model to distinguish similar trash features is strengthened by adding the convolutional block attention module (CBAM) to the enhanced feature extraction network. Finally, the design of the involution residual head structure in the detection head reduces the gradient disappearance and accelerates the convergence of the model loss values allowing the model to perform better classification and regression of the acquired feature layers. In this study, YOLOX-S is chosen as the baseline for each enhancement experiment. The experimental results show that compared with the baseline algorithm, the mean average precision (mAP) of i-YOLOX is improved by 1.47%, the number of parameters is reduced by 23.3%, and the FPS is improved by 40.4%. In practical applications, this improved model achieves accurate recognition of trash in natural scenes, which further validates the generalization performance of i-YOLOX and provides a reference for future domestic trash detection research.

This study used the strengthening grinding process (SGP) to treat the surface of 30CrMnSiA bearing steels. The effect of the jet angle of SGP on the tribological properties of 30CrMnSiA bearing steels under lubrication was investigated. The principle of enhancing wear resistance of 30CrMnSiA bearing steel ascribed to SGP was discussed in detail. The results showed that the lubrication properties and surface hardness of the 30CrMnSiA steels were enhanced due to the formation of numerous microscale microscope oil pockets on the surface layer and the grain refinement of the surface microstructures, resulting in a significant improvement in wear resistance. With the jet angle of SGP increased from 0° to 90°, the friction coefficient, the wear volume, and the specific wear rate were exhibited to reduce rapidly first, then reduce slowly, and then rise slowly. With the optimal parameters at the jet angle of 60°, compared with the control sample, the average friction coefficient was reduced from 0.2235 to 0.1609, and the wear volume and specific wear rate were reduced from 9.04 × 10⁻³ mm³ to 3.82 × 10⁻³ mm³ and from 15.13 × 10⁻³ mm²/N to 6.36 × 10⁻³ mm²/N, respectively. When the jet angle was 90°, the reduced wear resistance was mainly attributed to the excessive roughness that caused the oil coating on the surface to be severely damaged.

Rotary piezoelectric motors based on converse piezoelectric effect are very competitive in the fields of precision driving and positioning. Miniaturization and larger output capability are the crucial design objectives, and the efforts on structural modification, new materials application and optimization of control systems are persistent but the effectiveness is limited. In this paper, the resonance rotor excited by stator is investigated and the meshing drive mechanism of double traveling waves is proposed. Based on the theoretical analysis of bending vibration, the finite element method (FEM) is used to compare the modal shape and modal response in the peripheric, axial, and radial directions for the stator and three rotors. By analyzing the phase offset and vibrational orientation of contact particles at the interface, the principle of meshing traveling waves is discussed graphically and the concise formula obtaining the output performance is summarized, which is analogous with the principles of gear connection. Verified by the prototype experimental results, the speed of the proposed motor is the sum of the velocity of the stator’s contact particle and the resonance rotor’s contact particle, while the torque is less than twice the motor using the reference rotor.