Accurate estimation of hazelnut yield is crucial for optimizing resource management and harvest planning. Although the number of female flowers on a flowering plant is a reliable indicator of annual production, counting them remains difficult because of their extremely small size and inconspicuous shape and color. Currently, manual flower counting is the only available method, but it is time-consuming and prone to errors. In this study, a novel vision-based method for automatic flower counting specifically designed for hazelnut plants (Corylus avellana) exploiting a commercial high-resolution imaging system and an image-tiling strategy to enhance small-object detection is proposed. The method is designed to be fast and scalable, requiring less than 8 s per plant for processing, in contrast to 30–60 min typically required for manual counting by human operators. A dataset of 2000 labeled frames was used to train and evaluate multiple female hazelnut flower detection models. To improve the detection of small, low-contrast flowers, a modified YOLO11x architecture was introduced by adding a P2 layer, improving the preservation of fine-grained spatial information and resulting in a precision of 0.98 and a Mean Average Precision (mAP@50-95) of 0.89. The proposed method has been validated on images collected from hazelnut groves and compared with manual counting by four experienced operators in the field, demonstrating its ability to detect small, low-contrast flowers despite occlusions and varying lighting conditions. A regression-based bias correction was applied to compensate for systematic counting deviations, further improving accuracy and reducing the mean absolute percentage error to 27.44%, a value comparable to the variability observed in manual counting. The results indicate that the system can provide a scalable and efficient alternative to traditional female flower manual counting methods, offering an automated solution tailored to the unique challenges of hazelnut yield estimation.

The agricultural sector faces significant challenges in workforce training due to labor shortages, high costs, and seasonal constraints. To address these issues, this study presents the design and evaluation of an immersive hazelnut pruning simulator developed within the DEMETRA research project. The simulator leverages Virtual Reality (VR) and mechatronics to provide an interactive and realistic training environment for agricultural operators. Using high-fidelity 3D reconstructions created through photogrammetry and laser scanning, the system offers an engaging and lifelike experience. Key features include a step-by-step virtual instructor, realistic pruning tool interactions, full-body motion reconstruction for enhanced immersion, and markerless Human Pose Estimation (HPE) for ergonomic studies. The system also incorporates branch-cutting mechanics and an omnidirectional treadmill for unrestricted movement. Preliminary evaluations highlight the potential of this technology to improve skill acquisition and training efficiency in agriculture, offering a scalable solution for operator education.

Many neurological conditions, including stroke, Parkinson’s disease, amyotrophic lateral sclerosis, and mild cognitive impairment, compromise cognitive and motor functions and may threaten autonomy in complex daily activities. Rehabilitation can therefore benefit from tasks that simultaneously engage these domains. Driving is a representative example, as it combines continuous motor control, visual processing, attention, decision making, and reaction to unexpected events. Driving simulators can provide a controlled and repeatable environment in which these cognitive and motor functions can be jointly trained, while also supporting the assessment and recovery of driving-related abilities when return to driving is clinically relevant. However, rehabilitation-oriented simulators often provide limited multisensory feedback compared with professional systems, whose size, cost, and complexity limit their transfer to clinical settings. This study presents a compact, three-degree-of-freedom dynamic driving simulator developed for rehabilitation-oriented exergames with adjustable difficulty and measurable in-game metrics. It combines a parallel motion platform, haptic steering, instrumented pedals, and a modular software architecture based on a multibody simulation engine and a graphics engine, with automated session reports. Four exergames were evaluated in 62 healthy licensed adults aged 19–30 years, with each exergame scheduled twice. Traffic Mode was assessed in a separate data-collection campaign involving 38 participants, with partial participant overlap between campaigns. Slalom showed a marked ceiling effect, whereas Linekeeping showed a graded distribution of performance. Reaction Test outcomes varied widely between participants and were strongly associated with approach speed. Participant rank order was moderately preserved across executions. The results characterize the proposed measures in a homogeneous healthy population and identify the most informative metrics and the task configurations requiring refinement. Their suitability for longitudinal monitoring in patients remains to be established.