Agriculture is a labor-intensive industry. However, with the demographic shift toward an aging population, agriculture is increasingly confronted with a labor shortage. The technology for autonomous operation of agricultural equipment in large fields can improve productivity and reduce labor intensity, which can help alleviate the impact of population aging on agriculture. Nevertheless, significant challenges persist in the practical application of this technology, particularly concerning adaptability, operational precision, and efficiency. This review seeks to systematically explore the advancements in unmanned agricultural operations, with a focus on onboard environmental sensing, full-coverage path planning, and autonomous operational control technologies. Additionally, this review discusses the challenges and future directions of key technologies for the autonomous operation of agricultural equipment in large fields. This review aspires to serve as a foundational reference for the development of autonomous operation technologies for large-scale agricultural equipment.

Design and experiment of fuzzy-PID based tillage depth control system for a self-propelled electric tiller

In response to challenges such as inadequate driving stability and power in traditional weeding machinery, we designed and investigated a hydraulic chassis tailored for paddy field operations. Utilizing SolidWorks and RecurDyn V9R4 software, we obtained linear driving and steering curves to model and simulate the dynamics of the mower chassis. Through the AMESim software, we further modeled and simulated the hydraulic chassis system, focusing on the hydraulic characteristics of the components relevant to its operation. Subsequently, we developed a hydraulic-driven paddy weeder and conducted tests to evaluate the linear deviation and paddy slip rates. Our findings indicate that the designed hydraulic weeder chassis exhibits commendable dynamic performance and driving stability, with the actual average deviation and paddy slip rates measured at 2.61% and 3.59%, respectively. These results underscore the efficacy of our approach in addressing the challenges inherent in traditional weeding machinery and highlight the potential of hydraulic systems in enhancing agricultural operations.

Accurate path tracking is crucial for greenhouse robots operating in complex environments. However, traditional curve tracking algorithms suffer from low tracking accuracy and large tracking errors. This study aim to develop a high precision greenhouse autonomous following robot, use ANSYS Workbench 19.2 to perform stress and deformation analysis on the robot, then propose a path tracking method based on Linear Quadratic Regulator (LQR) to optimize the pure tracking to ensure high precision curved path tracking for curved tracking, finally perform a comparative simulation analysis in MATLAB R2024a. The structural analysis shows that the maximum equivalent stress is 196 MPa and the maximum deformation is 1.73 mm under a load of 600 kg, which are within the yield limit of 45 steel. Simulation results demonstrate that at a speed of 2 m/s, the conventional Pure Pursuit algorithm incurs a maximum lateral error of 0.3418 m and a heading error of 0.2669 rad under high curvature conditions. By contrast, the LQR–Pure Pursuit algorithm reduces the peak lateral error to 0.0904 m and confines the heading error to approximately 0.0217 rad. Experimental validation yielded an RMSE of 0.018 m for lateral error and 0.016 m for heading error. These findings confirm that the designed robot can sustain its payload under most operating scenarios and that the proposed tracking strategy effectively suppresses deviations and improves path-following accuracy.