Finding appropriate shaking parameters is crucial in designing effective mechanical harvesters. The maximum fruit removal can be achieved when the machine operator properly adjusts the amplitude and frequency for shaking each tree. This review covers the progress in research and development over the past decades on using mechanical harvesters for nut trees, such as almonds, pistachios, walnuts, and hickories, with a specific focus on the natural frequency of individual trees. Furthermore, the reported values of shaking frequency and amplitude from previous studies were discussed and compared, along with frequency calculation approaches based on various shaking mechanisms. Additionally, other parameters, such as clamping force, height, and shaking amplitude, were investigated to determine optimal values for minimizing tree damage. This review emphasizes that the tree’s diameter, height, and canopy morphology should be the primary factors considered when estimating the optimal shaking frequency for nut trees. It also highlights that, to date, the shaking amplitude, frequency, and duration set by field managers or machine operators tend to remain consistent for all trees, which can limit harvesting efficiency. The findings suggest that selecting these parameters uniformly across all trees may not result in efficient fruit removal for individual trees. However, with the assistance of modern computing technology and its adaptation for in-field applications, it is feasible to determine the optimal shaking frequency for each tree mathematically. This approach can maximize fruit removal rates while minimizing tree damage. Finally, the review suggests that improving existing harvesting machines by incorporating better vibratory patterns could offer benefits such as enhanced productivity, reduced labor costs, and decreased permanent tree damage.

Accurate power consumption forecasting is essential for energy-aware navigation and mission planning in mobile robots operating in agricultural environments. In this study, we present a comprehensive data-driven framework for predicting power usage, based on a dataset collected from 72 real-world trials performed in an orchard. These trials cover three distinct navigation patterns: rectangular paths, capsule-shaped circuits, and unstructured trajectories reflecting real-world field operations. The proposed forecasting approach integrates recent telemetry data with static mission parameters through an extended ensemble multi-head deep learning architecture. Several models are evaluated, including linear networks, convolutional neural networks, and recurrent architectures such as gated recurrent units and long short-term memory networks. Among these, the linear model consistently demonstrated the most stable cross-route performance, whereas the convolutional neural network achieved the lowest prediction error on structured routes. The results show that short-horizon forecasts maintained prediction errors below 20 percent for structured paths, whereas long-horizon predictions based solely on prior power measurements remained within 40 percent on average. These findings demonstrate the potential of lightweight, interpretable models for practical energy forecasting in agricultural mobile robotics. This work highlights the importance of combining static and dynamic features to improve power estimation and enhance robot autonomy in off-road environments.