With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development.

Since the hydraulic axial piston pump is the engine that drives hydraulic transmission systems, it is widely utilized in aerospace, marine equipment, civil engineering, and mechanical engineering. Operating safely and dependably is crucial, and failure poses a major risk. Hydraulic axial piston pump malfunctions are characterized by internal concealment, challenging self-adaptive feature extraction, and blatant timing of fault signals. By completely integrating the time-frequency feature conversion capability of synchrosqueezing wavelet transform (SWT), the feature extraction capability of VGG11, as well as the feature memory capability of the long short-term memory (LSTM) model, a novel intelligent fault identification method is proposed in this paper. First, the status data are transformed into two dimensions in terms of time and frequency by using SWT. Second, the depth features of the time–frequency map are obtained and dimensionality reduction is carried out by using the deep feature mining capability of VGG11. Third, LSTM is added to provide the damage identification model for long-term memory capabilities. The Softmax layer is utilized for the intelligent evaluation of various damage patterns and health state. The proposed method is utilized to identify and diagnose five typical states, including normal state, swash plate wear, sliding slipper wear, loose slipper, and center spring failure, based on the externally observed vibration signals of a hydraulic axial piston pump. The results indicate that the average test accuracy for five typical state signals reaches 99.43%, the standard deviation is 0.0011, and the average test duration is 2.675 s. The integrated model exhibits improved all-around performance when compared to LSTM, LeNet-5, AlexNet, VGG11, and other typical models. The proposed method is validated to be efficient and accurate for the intelligent identification of common defects of hydraulic axial piston pumps.

The effective way to improve the reliability of the fuel metering system in an aero-engine is to use a high-speed on/off valve (HSV) instead of a servo valve as the pilot stage of the fuel metering valve. However, the dynamic performance of the fuel metering valve is easily affected by the HSV, so a novel two-stage high-speed on/off valve with adjustable maximum opening (AMOHSV) is proposed in which the maximum stroke of the main valve is regulated with an adjustable rod. Firstly, the structure and working principle of the proposed valve are presented. Then, an entire mathematical model is established and verified based on a multi-physical field coupling mechanism. Finally, simulations and experiments prove that when the maximum opening is 0.2 mm, the total opening time and total closing time of the AMOHSV are within 5 ms. In addition, an upward inflection point and a downward inflection point on the pressure curve of the control chamber can be used to identify the total opening time and total closing time, respectively. The research results also prove that the proposed structure solves the conflict between the maximum flow rate and the dynamic performance of the traditional HSV.

The dual-input single-output (DI-SO) cylindrical spur gear system possesses advantages such as high load-carrying capacity, precise transmission, and low energy loss. It is increasingly becoming a core component of power transmission systems in maritime vessels, aerospace, marine engineering, and construction machinery. In practical operation, the stability of the DI-SO cylindrical spur gear system is influenced by complex excitations. These excitations lead to nonlinear vibration, meshing instability, and noise, which affect the performance and reliability of the entire equipment. Hence, the dynamic performance of the DI-SO cylindrical spur gear system is thoroughly investigated in this research. The impact of excitations and nonlinear factors on the dynamic characteristics was investigated comprehensively. A comparative analysis of the gear system was conducted by establishing a bending–torsional coupling vibration analysis model under synchronous and asynchronous meshing conditions. Nonlinear factors such as periodic time-varying meshing stiffness, meshing damping, friction coefficient, friction arms, load sharing ratio, comprehensive transmission error, and backlash were considered in the proposed model. Then, the effect laws of excitations and nonlinear factors such as meshing frequency, driving load fluctuation, backlash, and comprehensive transmission error were analyzed. The results indicate that the dynamic characteristics exhibited staged stable and unstable states under different meshing frequencies and meshing conditions. At the medium-frequency meshing stage (0.96 × 10⁴~1.78 × 10⁴ Hz), alternating phenomena of multi-periodic, quasi-periodic, and chaotic motion states were observed. Moreover, the root mean square value (RMS) of the dynamic transmission error (DTE) in the asynchronized gear system was generally higher than that in the synchronized gear system. It was found that selecting the appropriate meshing condition could effectively reduce the amplitude of the DTE. Additionally, the dynamic performance could be significantly improved by adjusting control parameters such as driving load fluctuation (0~179 N), backlash (0.8 × 10⁻⁴~0.9 × 10⁻⁴ m), and comprehensive transmission error (7.9 × 10⁻⁴~9.4 × 10⁻⁴ m). The research results provide a theoretical guidance for the design and optimization of the DI-SO cylindrical spur gear system.

Against the backdrop of growing global water scarcity and mounting food security pressures, the intelligent transformation of agricultural irrigation and drainage machinery is crucial for enhancing operational efficiency, reducing energy consumption, and ensuring reliable field operations. This paper systematically reviews the evolution of agricultural irrigation and drainage machinery from traditional manual operation and mechanized practices to automated control and intelligent decision-making. Within a closed-loop “sensing–decision-making–execution” framework, particular emphasis is placed on key technologies, including multisource sensing, intelligent decision-making, adaptive actuation, and precision control. The findings indicate that multiscale, multimodal information fusion enhances the observability of farmland environments and equipment conditions. Irrigation and drainage decision-making is shifting from timer-based, threshold-based, and empirical-rule-based approaches toward state prediction, mechanism-constrained control, and multi-objective optimization. Precision irrigation is advancing from state monitoring to predictive decision-making and closed-loop intelligent control. However, there are still key challenges such as data heterogeneity, insufficient cross-scenario model generalizability, and limitations in model interpretability and real-time performance. Future research should focus on integrated irrigation and drainage, multimodal spatiotemporal fusion, mechanism and data collaborative driving, cloud–edge–end collaborative intelligence, and standardized field verification to enhance the autonomous decision-making, precision operation, and long-term reliability of agricultural irrigation and drainage machinery in complex environments.

Hydraulic transmission and control technology is a core supporting technology for agricultural machinery. Its technical performance directly determines the operation quality, production efficiency, and overall reliability of agricultural machinery. This paper focuses on hydraulic transmission and control technology for agricultural machinery. It systematically reviews the field from three dimensions: key technology analysis, research status review, and development trend outlook. First, it elaborates the three core technical systems in agricultural machinery hydraulics. In terms of hydraulic components, it analyzes the working principles and application scenarios of power elements, actuating elements, and control elements. In terms of hydraulic systems, it analyzes the technical characteristics and evolution paths of five typical systems used in agricultural machinery. In terms of control technology, it summarizes a multi-level technical framework. This framework includes classical PID and its improved algorithms, advanced control methods, and intelligent control methods. Second, based on the latest domestic and international research literature, it comprehensively reviews the research progress in hydraulic components, hydraulic systems, and control methods. Finally, in response to the transformation and upgrading of smart agriculture and the “dual carbon” strategy, it identifies three major future directions for hydraulic transmission and control technology in agricultural machinery. The findings of this review are expected to offer theoretical references and technical guidance for the R&D and industrial upgrading of high-end hydraulic systems for agricultural machinery.