Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems.

Severe plastic deformation (SPD) processing such as equal channel angular extrusion (ECAE) has been pioneered to produce ultrafine grained (UFG) metals for improved mechanical and physical properties. However, understanding the machining of SPD-processed metals is still limited. This study aims to investigate the differences in chip morphology when machining ECAE-processed UFG and coarse-grained (CG) titanium (Ti) and understand the chip formation mechanism using metallographic analysis, digital imaging correlation (DIC), and nano-indentation. The chip morphology is classified as aperiodic saw-tooth, continuous, or periodic saw-tooth, and changes with the cutting speed. The chip formation mechanism of the ECAE-processed Ti transitions from cyclic shear localization within the low cutting speed regime (such as 0.1 m/s or higher) to uniform shear localization within the moderately high cutting speed regime (such as from 0.5 to 1.0 m/s) and to cyclic shear localization (1.0 m/s). The shear band spacing increases with the cutting speed and is always lower than that of the CG counterpart. If the shear strain rate distribution contains a shift in the chip flow direction, the chip morphology appears saw-tooth, and cyclic shear localization is the chip formation mechanism. If no such shift occurs, the chip formation is considered continuous, and uniform shear localization is the chip formation mechanism. Hardness measurements show that cyclic shear localization is the chip formation mechanism when localized hardness peaks occur, whereas uniform shear localization is operative when the hardness is relatively constant.

Typically, developing new digital agriculture technologies requires substantial on-site resources and data. However, the crop’s growth cycle provides only limited time windows for experiments and equipment validation. This study presents a photorealistic digital twin of a commercial-scale strawberry farm, coupled with a simulated ground vehicle, to address these constraints by generating high-fidelity synthetic RGB and LiDAR data. These data enable the rapid development and evaluation of a deep learning-based machine vision pipeline for fruit detection and sizing without continuously relying on real-field access. Traditional simulators often lack visual realism, leading many studies to mix real images or adopt domain adaptation methods to address the reality gap. In contrast, this work relies solely on photorealistic simulation outputs for training, eliminating the need for real images or specialized adaptation approaches. After training exclusively on images captured in the virtual environment, the model was tested on a commercial-scale strawberry farm using a physical ground vehicle. Two separate trials with field images resulted in F1-scores of 0.92 and 0.81 for detection and a sizing error of 1.4 mm (R² = 0.92) when comparing image-derived diameters against caliper measurements. These findings indicate that a digital twin-driven sim2real transfer can offer substantial time and cost savings by refining crucial tasks such as stereo sensor calibration and machine learning model development before extensive real-field deployments. In addition, the study examined geometric accuracy and visual fidelity through systematic comparisons of LiDAR and RGB sensor outputs from the virtual and real farms. Results demonstrated close alignment in both topography and textural details, validating the digital twin’s ability to replicate intricate field characteristics, including raised bed geometry and strawberry plant distribution. The techniques developed and validated in this strawberry project have broad applicability across agricultural commodities, particularly for fruit and vegetable production systems. This study demonstrates that integrating digital twins with simulation tools can significantly reduce the need for resource-intensive field data collection while accelerating the development and refinement of agricultural robotics algorithms and hardware.

SUMMARY It is common in robot tracking control that controllers are designed based on the exact kinematic model of the robot manipulator. However, because of measurement errors and changes of states in practice, the original kinematic model is often no longer accurate and will degrade the control result. An adaptive backstepping controller is designed here for parallel robot systems with kinematics and dynamics uncertainties. Backstepping control is used to manage the transformation between the errors in task space and joint space. Adaptive control is utilized to compensate for uncertainties in both dynamics and kinematics. The controller demonstrated good performance in simulation.

Empirical mathematical models of cutting forces in machining processes use experimentally determined input parameters to make predictions. A general method for propagation of input parameter uncertainties through such predictive models is developed. Sources of uncertainty are identified and classified. First, a classical uncertainty procedure is employed to estimate uncertainties associated with the data reduction equation using a first-order Taylor series expansion. Small values of input parameter uncertainties justify this local linearization. Coverage factors required to estimate confidence intervals are computed based on appropriate underlying statistical distributions. A root sum of squares method yields the overall expanded uncertainty in force predictions. A popular model used for predicting cutting forces in end milling is selected to demonstrate the procedure, but the demonstrated approach is general. The analysis is applied to experimental data. Force predictions are quoted along with a confidence interval attached to them. An alternative analysis based on Monte Carlo simulations is also presented. This procedure yields different insights compared with the classical uncertainty analysis and complements it. Monte Carlo simulation provides combined uncertainties directly without sensitivity calculations. Classical uncertainty analysis reveals the impacts of random effects and systematic effects separately. This information can prompt the user to improve the experimental setup if the impact of systematic effects is observed to be comparatively large. The method of quoting an estimate of the uncertainty in force predictions presented in this paper will permit users to assess the suitability of given empirical force prediction models in specific applications.