Filippo Orazi

Automated cucumber harvesting requires a perception system that can detect fruit, assess ripeness and localize the peduncle for cutting under real commercial greenhouse conditions. This study proposes an RGB-D perception framework combining YOLOv8n object detection with depth-based geometric estimation. The framework was evaluated on data collected in commercial greenhouses with dense, continuously varying occlusion, variable illumination and multiple co-visible fruits at different maturity stages. Detections were assigned to five operational classes: ripe, unripe, flagged (non-marketable due to overripeness or deformity), obstructed and peduncle. The detector achieved an overall mAP50 of 0.568, but appearance-based ripeness classification proved unreliable. Confusion concentrated along two boundaries: a continuous, judgment-based occlusion criterion and an absolute length threshold that RGB appearance cannot directly resolve. Depth-based length estimation, by contrast, achieved an MAE of 0.72 cm (RMSE = 1.15 cm, MAPE = 4.04%) directly in the greenhouse. Applying the ripeness length threshold to the estimated lengths misclassified 6.4% of fruits at the RIPE/UNRIPE boundary on the dimensional validation set, indicating that length-based ripeness assignment is feasible. Diameter estimation was markedly less accurate (R² = 0.49) and is not proposed as a validated measurement for grading or yield estimation. The framework also estimated candidate 3D cutting points that remained spatially consistent across frames; cutting accuracy requires future robotic validation. These results suggest that, in complex greenhouse environments, ripeness decisions of cucumber should rely on depth-based geometric measurement, with object detection used primarily for fruit and peduncle localization. Combining visual detection with quantitative measurement therefore appears to be a promising basis for robotic cucumber harvesting.

Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization.

The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QSplines) have recently been proposed to approximate quantum activation functions to introduce non-linearity in quantum algorithms. However, QSplines make use of the HHL as a subroutine and require a fault-tolerant quantum computer to be correctly implemented. This work proposes the Generalised Hybrid Quantum Splines (GHQSplines), a novel method for approximating non-linear quantum activation functions using hybrid quantum-classical computation. The GHQSplines overcome the highly demanding requirements of the original QSplines in terms of quantum hardware and can be implemented using near-term quantum computers. Furthermore, the proposed method relies on a flexible problem representation for non-linear approximation and it is suitable to be embedded in existing quantum neural network architectures. In addition, we provide a practical implementation of the GHQSplines using Pennylane and show that our model outperforms the original QSplines in terms of quality of fitting.