Abstract

Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.

Abstract
The integration of Deep Learning techniques for grapevine yield estimation has led to significant advancements in Precision Viticulture. The accurate detection and counting of berries per bunch is a critical task that can explain up to 30% of yield variability, thereby enabling improved yield estimation. This study proposes a YOLO-based approach for the automated detection and counting of visible grapevine berries, using a dataset of more than 1500 images collected over three phenological stages. The selected YOLO models performed well in both detection and counting tasks, with all models achieving high detection accuracy (G-mAP ' 0.95) and estimation of visible berries (R2 ' 0.97). Among the evaluated models, YOLOv11n exhibited the highest detection performance (F1-Score = 0.954, G-mAP = 0.962), while YOLOv10n demonstrated the most consistent and reliable counting accuracy (MAPE = 4.764, MSE = 12.203, RMSE = 3.493). Beyond overall performance, the analysis revealed that ampelographic features such as berry size, occlusion, and bunch morphology can influence accuracy, although YOLOv10n showed no significant disparities across categories. To extend the scope, a complementary analysis demonstrated a strong linear relationship (R2 = 0.860) between visible counts and the total number of berries per bunch, supporting the potential of correction models to address occlusion. By systematically evaluating model behaviour across diverse viticultural conditions and incorporating correlation with total berry counts, this study provides a deeper understanding of the robustness and limitations of Deep Learning models, offering critical insights for future applications in vineyard monitoring, yield estimation, harvest optimisation, and management. © 2026 The Authors.

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The European Project Semester (EPS), offered by the Instituto Superior de Engenharia do Porto (ISEP), is a capstone programme designed for undergraduate students in engineering, product design, and business. EPS@ISEP fosters project-based learning, promotes multicultural and interdisciplinary teamwork, and ethics- and sustainability-driven design. This study applies Natural Language Processing techniques, specifically text mining, to analyse project papers produced by EPS@ISEP teams. The proposed method aims to identify evidence of ethical concerns within EPS@ISEP projects. An innovative keyword mapping approach is introduced that first defines and refines a list of ethics-related keywords through prompt engineering. This enriched list of keywords is then used to systematically map the content of project papers. The findings indicate that the EPS@ISEP robotics project papers analysed demonstrate awareness of ethical considerations and actively incorporate them into design processes. The method presented is adaptable to various application areas, such as monitoring compliance with responsible innovation or sustainability policies.

Abstract
The wine production, included in the primary sector is a great cultural and economic deal, both nationally and internationally matters. However, it is highly dependent on natural resources, and traditionally involves high energy and water consumption. Given the global climate change scenario and the need for efficient resource management, it is necessary to implement a sustainable plan for the wine sector to realize sustainable practices. Data from the International Organization of Vine and Wine (OIV), states that global wine production exceeded 260 million hectoliters, in 2022. These has resulted in significant water and energy consumption, with around 500-1200 m(3) of water used per hectare for irrigation and 1.2 gigajoules per hectoliter of wine produced, concluding that more than 80% of total water consumption is associated with irrigation, while more than 90% of energy consumption, is associated with winery processes. In this context, the scarcity of water or the need to achieve carbon neutrality by 2050 makes it essential to adopt energy and water efficiency measures that allow for the sustainable management of resources without endangering the sector's viability. With this in mind, a case study applied to a Portuguese wine industry is presented, including data analysis from water and energy consumption. Also, efficiency metrics will be analyzed, proposing management and decision-support tools based on monitoring and sensor-based techniques. In fact, one example of these efficiency measures deals with the adoption of systems that provide real-time data on consumption patterns and resource availability in order to improve sustainability of the global process production.