Abstract: Mechanical harvesting has been widely used in recent years, due mainly to the ever-increasing fruit demand and the labor shortages. Fortunately, the numerical simulation can be expected to analyze the response of the fruits to the compression, vibration, and impact under various mechanical loads and environment. Among them, finite element analysis (FEA) can be utilized to predict the complex mechanical behavior, thereby providing for an efficient, low-cost, and high-precision solution for fruit harvesting. In this study, a systematic review was presented on the research progress of the fruit harvesting using FEA. Firstly, the current status of the mechanized harvesting was outlined, including the vibration harvesting, harvest assist platforms, and picking robots. Secondly, the research process of the finite element modeling was presented during fruit harvesting. In geometric modeling, 3D scanning and reverse engineering were typically adopted to reconstruct the accurate models of the fruits, branches, and harvesting equipment. The fruit physical properties were acquired to determine the key indicators, such as the elastic modulus, Poisson's ratio, and yield strength after mechanical tests (e.g., uniaxial compression, tensile, and impact tests). These parameters were directly determined the accuracy of the simulation. Mesh generation was often required to balance the computational efficiency and accuracy for the reliability of the simulations. Thirdly, two fields of the FEA application were focused on the fruit rheological properties and the optimization of the harvesting equipment. In rheological properties, the FEA was used to simulate the static stress (e.g., fruit stacking during storage) and dynamic stress (e.g., impact during vibration harvesting or robotic picking). The stress distribution was then obtained to identify the high-risk damage areas. In terms of the equipment optimization, the FEA was employed to adjust the parameters of the vibration harvesting (frequency, amplitude, and excitation direction), in order to reduce the fruit damage. The structural components of the robotic end-effectors (e.g., flexible grippers made of silica gel or rubber) were optimized to minimize the contact stress. The picking paths were also simulated to improve the operational efficiency of the robotic arms. Finally, the current challenges were summarized for the future directions, such as the balance between model simplification and simulation accuracy, insufficient adaptability of the boundary conditions in the dynamic operation scenarios, and the lack of standardized databases for the fruit attributes and working parameters. The efficiency of the FEA was then restricted in the practical applications. Multi-physics coupling, artificial intelligence (AI) and FEA were integrated for the databases of the cross-variety fruit attributes in the future. Greater breakthroughs were found, such as the accurate prediction on the harvesting damage under complex working conditions, intelligent optimization of the adaptive picking equipment, and the performance of the multi-machine collaborative operation. Furthermore, the model iteration and verification were combined with the field test, in order to strengthen the connection between numerical simulation and engineering practice. More solid theoretical and technical support were also provided for the large-scale application of the mechanic harvesting. The fruit industry can transform and then upgrade towards the high efficiency, low damage and intelligence. Numerical simulation can also provide the efficient and feasible solution in fruit harvesting.