Abstract
The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity. Antenna defects, ranging from manufacturing imperfections to environmental wear, pose significant challenges to the reliability and performance of communication systems. This review paper navigates the landscape of antenna defect detection, emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection. This review paper serves as a valuable resource for researchers, engineers, and practitioners engaged in the design and maintenance of communication systems. The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures. In this study, a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented. The PRISMA principles will be followed throughout the review, and its goals are to provide a summary of recent research, identify relevant computer vision techniques, and evaluate how effective these techniques are in discovering defects during inspections. It contains articles from scholarly journals as well as papers presented at conferences up until June 2023. This research utilized search phrases that were relevant, and papers were chosen based on whether or not they met certain inclusion and exclusion criteria. In this study, several different computer vision approaches, such as feature extraction and defect classification, are broken down and analyzed. Additionally, their applicability and performance are discussed. The review highlights the significance of utilizing a wide variety of datasets and measurement criteria. The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation, such as real-time inspection systems and multispectral imaging. This review, on its whole, offers a complete study of computer vision approaches for quality control in antenna parts. It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Abstract
Growing obesity has been a worldwide issue for several years. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be difficult. Today, people have a very fast-paced life and sometimes neglect food choices. In order to simplify the interpretation of the Nutri-score labeling this paper proposes a method capable of automatically reading food labels with this format. This method is intended to support users when choosing the products to buy based on the letter identification of the label. For this purpose, a dataset was created, and a prototype mobile application was developed using a deep learning network to recognize the Nutri-score information. Although the final solution is still in progress, the reading module, which includes the proposed method, achieved an encouraging and promising accuracy (above 90%). The upcoming developments of the model include information to the user about the nutritional value of the analyzed product combining it’s Nutri-score label and composition. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
The determination of grape quality parameters is intricately linked to the mineral composition of the fruit; this relationship is increasingly affected by the impacts of climate change. The conventional chemical methodologies employed for the mineral quantification of grape tissues are expensive and impracticable for widespread commercial applications. This paper utilized Laser-Induced Breakdown Spectroscopy (LIBS) to analyze the mineral constituents within the skin, pulp, and seeds of two distinct Vitis vinifera cultivars: a white cultivar (Loureiro) and a red cultivar (Vinh & atilde;o). The primary objective was to discriminate the potential variations in the calcium (Ca), magnesium (Mg), and nitrogen (N) concentrations and water content among different grape tissues, explaining their consequential impact on the metabolic constitution of the grapes and, by extension, their influence on various quality parameters. Additionally, the study compared the mineral contents of the white and red grape cultivars across three distinct time points post veraison. Significant differences (p < 0.05) were observed between the Loureiro and Vinh & atilde;o cultivars in Ca concentrations across all the dates and tissues and for Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin and pulp. In the Vinh & atilde;o cultivar, Ca differences were found in the pulp across the dates, N in the seeds, and water content in the skin, pulp, and seeds. Comparing the cultivars within tissues, Ca exhibited differences in the pulp, Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin, pulp, and seeds. These findings provide insights into the relationship between the grape mineral and water content, climatic factors, and viticulture practices within a changing climate.

Abstract
Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.

Abstract
Synthetic aperture radar (SAR) technology has emerged as a pivotal tool in viticulture, offering unique capabilities for various applications. This study provides a comprehensive overview of the current state-of-the-art applications of SAR in viticulture, highlighting its significance in addressing key challenges and enhancing viticultural practices. The historical evolution and motivations behind SAR technology are also provided, along with a demonstration of its applications within viticulture, showcasing its effectiveness in various aspects of vineyard management, including delineating vineyard boundaries, assessing grapevine health, and optimizing irrigation strategies. Furthermore, future perspectives and trends in SAR applications in viticulture are discussed, including advancements in SAR technology, integration with other remote sensing techniques, and the potential for enhanced data analytics and decision support systems. Through this article, a comprehensive understanding of the role of SAR in viticulture is provided, along with inspiration for future research endeavors in this rapidly evolving field, contributing to the sustainable development and optimization of vineyard management practices.