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.

A fuel metering valve actuated by two binary-coded digital valve arrays (BDVAs) is proposed to improve the reliability of conventional fuel metering valves piloted by a servo valve. The design concept of this configuration is obtained from the structural characteristics of the dual nozzle-flapper and the flow regulation method of the digital hydraulic technology. The structure and working principle of the fuel metering valve are presented. Then, a mathematical model of the entire valve is developed for dynamic analysis. Subsequently, the mechanism of the transient flow uncertainty of the BDVA is revealed through simulation to determine the fluctuation in the velocity of the fuel metering valve. Furthermore, step response indicates that the delay time of the fuel metering valve is within 4.1 ms. Finally, to improve the position tracking accuracy of the fuel metering valve, a velocity feedforward proportional-integral controller with pulse code modulation is proposed. A series of comparative analyses indicate that compared with those of the velocity feedforward controller, the average and standard deviation of the position error for the proposed controller are reduced by 78 and 72.7%, respectively. The results prove the feasibility of the proposed valve and the effectiveness of the proposed control strategy.

This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a hydraulic servo system. An intelligent control method is designed that uses self-learning without a model or prior knowledge, in order to achieve certain control effects. The control quantity can be obtained at the current moment through the continuous iteration of a strategy–value network, and the online self-tuning of parameters can be realized. Taking the hydraulic servo system as the experimental object, a twin delayed deep deterministic (TD3) policy gradient was used to reinforce the learning of the system. Additionally, the parameter setting was compared using a deep deterministic policy gradient (DDPG) and a linear–quadratic–Gaussian (LQG) based on linear quadratic Gaussian objective function. To compile the reinforcement learning algorithm and deploy it to the test platform controller for testing, we used the Speedgoat prototype target machine as the controller to build the fast prototype control test platform. MATLAB/Coder and compute unified device architecture (CUDA) were used to generate an S-function. The results show that, compared with other parameter tuning methods, the proposed algorithm can effectively optimize the controller parameters and improve the dynamic response of the system when tracking signals.

A hydraulic pressure servo system based on two high-speed on/off valves (HSV) is a discontinuous system due to the discrete flow of HSV when driven by pulse width modulation (PWM) signal. Pressure variation in the testing chamber is determined by the flow rate difference between the charging and discharging HSV. In this paper, a pressure controller consisting of a differential PWM (DPWM) scheme, asymmetric pressure difference compensation (APDC) and nonlinear adaptive control (NAC) is proposed to precisely control the pressure. The DPWM scheme is designed to improve the resolution of the net flow rate into the testing chamber. Furthermore, due to the strong asymmetry between the charging and the discharging process, the APDC method is proposed to design the two initial duty cycles of the DPWM signal which help to balance its charging and discharging ability under different working pressure points. Since the pressure system is a nonlinear, uncertain system due to oil compression and leakage, the NAC is designed to calculate the control duty cycle of the DPWM signal, which is used to overcome the unmodeled dynamic and parameter uncertainties. Comparative experiments indicate that the proposed controller can ensure good pressure tracking performance and enhance system robustness under different working pressure points and tracking frequencies.