Abstract
Ground Penetrating Radar (GPR) is a geophysical imaging technique used for the characterization of a sub surface’s electromagnetic properties, allowing for the detection of buried objects. The characterization of an object’s parameters, such as position, depth and radius, is possible by identifying the distinct hyperbolic signature of objects in GPR B-scans. This paper proposes an automated system to detect and characterize the presence of buried objects through the analysis of GPR data, using GPR and computer vision data pro cessing techniques and YOLO segmentation models. A multi-channel encoding strategy was explored when training the models. This consisted of training the models with images where complementing data processing techniques were stored in each image RGB channel, with the aim of maximizing the information. The hy perbola segmentation masks predicted by the trained neural network were related to the mathematical model of the GPR hyperbola, using constrained least squares. The results show that YOLO models trained with multi-channel encoding provide more accurate models. Parameter estimation proved accurate for the object’s position and depth, however, radius estimation proved inaccurate for objects with relatively small radii. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

Abstract
Yield forecasting is of immeasurable value in modern viticulture to optimize harvest scheduling and quality management. The number of inflorescences and flowers per vine is one of the main components and their assessment serves as an early predictor, which can explain up to 85-90% of yield variability. This study introduces a sophisticated framework that integrates the benchmark of different advanced deep learning and classic image processing to automate the segmentation of grapevine inflorescences and the detection of single flowers, to achieve precise, early, and non-invasive yield predictions in viticulture. The YOLOv8n model achieved superior performance in localizing inflorescences ( F1-Score (Box) = 95.9%) and detecting individual flowers (F1-Score = 91.4%), while the YOLOv5n model excelled in the segmentation task ( F1-Score (Mask) = 98.6%). The models demonstrated a strong correlation (R-2 > 90.0%) between detected and visible flowers in inflorescences. A statistical analysis confirmed the robustness of the framework, with the YOLOv8 model once again standing out, showing no significant differences in error rates across diverse grapevine morphologies and varieties, ensuring wide applicability. The results demonstrate that these models can significantly improve the accuracy of early yield predictions, offering a noninvasive, scalable solution for Precision Viticulture. The findings underscore the potential for Computer Vision technology to enhance vineyard management practices, leading to better resource allocation and improved crop quality.

Abstract
With the global population on the rise and a declining agricultural labor force, the realm of robotics research in agriculture, such as robotic manipulators, has assumed heightened significance. This article undertakes a comprehensive exploration of the latest advancements in controllers tailored for robotic manipulators. The investigation encompasses an examination of six distinct controller paradigms, complemented by the presentation of three exemplars for each category. These paradigms encompass: (i) adaptive control, (ii) sliding mode control, (iii) model predictive control, (iv) robust control, (v) fuzzy logic control and (vi) neural network control. The article further introduces and presents comparative tables for each controller category. These controllers excel in tracking trajectories and efficiently reaching reference points with rapid convergence. The key point of divergence among these controllers resides in their inherent complexity.

Abstract
Job shop scheduling problems are common in the engineering field. In spite of some approaches consider just the most important objective to optimize, several other conflicting criteria are also important. Multi-objective optimization algorithms can be used to solve these problems optimizing, simultaneously, two or more objectives. However, when the number of objectives increases, the problems become more challenging. This paper presents the results of the optimization of a set of job shop scheduling with unrelated parallel machines and sequence-dependent setup times, using the NSGA-III. Several instances with different sizes in terms of number of jobs and machines are considered. The goal is to assign jobs to machines in order to simultaneously minimize the maximum job completion time (makespan), the average job completion time and the standard deviation of the job completion time. These results are analysed and confirm the validity and highlight the advantages of this approach.