Abstract
The outbreak of the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been a watershed moment in human history, causing a profound shift in the global landscape that has affected every aspect of our lives [...]

Abstract
Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. We mainly aim to investigate the main source of the variability in the modelling errors for the flowering timings of two important varieties of vine in the Douro Demarcated Region (DDR) of Portugal; this is based on five phenology model simulations that use optimal parameters and that are estimated by three optimization algorithms (MLE, SA and SCE-UA). Our results indicate that the main source of the variability in calibration can be affected by the initially assumed parameter boundary. Restricting the initial parameter distribution to a narrow range impedes the algorithm from exploring the full parameter space and searching for optimal parameters. This can lead to the largest variation in different models. At an identified appropriate boundary, the difference between the two varieties represents the largest source of uncertainty, while the choice of algorithm for calibration contributes least to the overall uncertainty. The smaller variability among different models or algorithms (tools for analysis) compared to between different varieties could indicate the overall reliability of the calibration. All optimization algorithms show similar results in terms of the obtained goodness-of-fit: the RMSE (MAE) is 5-6 (4-5) days with a negligible mean bias and moderately good R-2 (0.5-0.6) for the ensemble median predictor. Nevertheless, a similar predictive performance can result from differently estimated parameter values, due to the equifinality or multi-modal issue in which different parameter combinations give similar results. This mainly occurs for models with a non-linear structure compared to those with a near-linear one. Yet, the former models are found to outperform the latter ones in predicting the flowering timing of the two varieties in the DDR. Overall, our findings highlight the importance of carefully defining the initial parameter boundary and decomposing the total variance of prediction errors. This study is expected to bring new insights that will help to better inform users about the importance of choice when these factors are involved in calibration. Nonetheless, the importance of each factor can change depending on the specific situation. Details of how the optimization methods are applied and of the continuous model improvement are important.

Abstract
The technological market is increasingly evolving as evidenced by the innovative and streamlined manufacturing processes. Printed Circuit Boards (PCB) are widely employed in the electronics fabrication industry, resorting to the Gerber open standard format to transfer the manufacturing data. The Gerber format describes not only metadata related to the manufacturing process but also the PCB image. To be able to map the electronic circuit pattern to be printed, a parser to convert Gerber files into a bitmap image is required. The current literature as well as available Gerber viewers and libraries showed limitations mainly in the Gerber format support, focusing only on a subset of commands. In this work, the development of a recursive descent approach for parsing Gerber files is described, outlining its interpretation and the renderization of 2D bitmap images. All the defined commands in the specification based on Gerber X2 generation were successfully rendered, unlike the tested commercial parsers used in the experiments. Moreover, the obtained results were comparable to those parsers regarding the commands they can execute as well as the ground-truth, emphasizing the accuracy of the proposed approach. Its top-down and recursive architecture allows easy integration with other software regardless of the platform, highlighting its potential inclusion and integration in the production of electronic circuits.

Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.

Abstract
Soil Moisture (SM) is one of the most critical factors for a crop's growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR's output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.