Abstract
In modern industrial environments, ensuring the quality of manufactured components is critical, particularly when dealing with reflective surfaces that hinder conventional inspection techniques. Although deep learning-based methods offer robust solutions for visual defect detection, their performance often hinges on the availability of substantial annotated datasets. In industrial scenarios, labeling such datasets is costly and time-consuming. This study investigates applying sample selection techniques to reduce annotation efforts for porosity detection on machined aluminium parts. Several selection strategies were evaluated using a real-world dataset composed of high-resolution images, including uncertainty, diversity, random-based criteria, and hybrid combinations. The best-performing strategy, which combined entropy-based uncertainty, spatial diversity, and random-based, achieved an F1-score of 86.70% and a recall of 82.99% after ten iterations using only 2,400 annotated images, corresponding to 66.67% of the active learning pool. Although the fully supervised model achieved an F1-score of 88.84% and a recall of 86.30%, the proposed approach proved a competitive alternative. These results demonstrate that selective data annotation can significantly reduce labeling effort while maintaining reliable performance in defect detection, even under the challenging conditions posed by reflective industrial parts.

Abstract
Robotic bin-picking is a critical operation in modern industry, which is characterised by the detection, selection, and placement of items from a disordered and cluttered environment, which can be boundary limited or not, e.g. bins, boxes or containers. In this context, perception systems are employed to localise, detect and estimate grasping points. Despite the considerable progress made, from analytical approaches to recent deep learning methods, challenges still remain. This is evidenced by the growing innovation proposing distinct solutions. This paper aims to review perception methodologies developed since 2009, providing detailed descriptions and discussions of their implementation. Additionally, it presents an extensive study, detailing each work, along with a comprehensive overview of the advancements in bin-picking perception.

Abstract
Textile-to-textile recycling is strongly constrained by upstream pre-processing, where post-consumer clothing must be identified, separated, and prepared under high variability in materials, appearance, and contamination. This paper presents a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic literature review of intelligent and automated technologies for textile recycling pre-processing covering the interval between 2015 to 2025. After screening and quality assessment, 21 primary studies published between 2020 and 2025 were included. The literature is synthesized across three task families: (i) identificationof fiber/material, composition, or color; (ii) sorting, considered only when explicit separation strategies are defined to operationalize identification outcomes into routing actions or output streams; and (iii) contaminant detection and/or removal, targeting non-recyclable items. Results show that identification dominates the field (19/21 studies), supported by Red-Green-Blue (RGB) and red-green-blue plus depth (RGB-D) imaging and material-signature sensing, including near-infrared (NIR) spectroscopy, hyperspectral imaging (HSI), and Raman spectroscopy. In contrast, sorting as a defined separation stage is less frequent (4/21), and contaminant-related automation remains sparse (3/21). Most studies are validated in laboratory conditions, with limited semi-industrial evidence, highlighting a persistent perception-to-action gap. Overall, the review indicates that robust separation strategies, representative datasets, and end-to-end system integration remain key bottlenecks for scalable automated textile recycling pre-processing.

Abstract
The Industry 5.0 concept guides the industry to the premise of sustainability, resilience and human-centric solutions. The last related pillar tries to create solutions to empower the people in production line processes since solutions should be designed to be easy to use and easy to learn without discarding the working people. In this regard, it's natural that robots become closer to humans in industrial applications where it is possible to absorb human-machine qualities. Robotic grasping has widespread application with a wide range of applicability. However, engineers and shop-floor operators spend time finding a fast response solution when the production demand changes. Aiming to create a tool to help this procedure in a human-centred fashion, the current paper proposes a programming-by-demonstration solution that is easy to use, reuse, adapt, and increment with its modular design.

Abstract
In 2023, global kiwifruit production reached over 4.4 million tonnes, highlighting the crop's significant economic importance. However, achieving high yields depends on adequate pollination. In Actinidia species, pollen is transferred by insects from male to female flowers on separate plants. Natural pollination faces increasing challenges due to the decline in pollinator populations and climate variability, driving the adoption of assisted pollination methods. This study examines the Portuguese kiwifruit sector, one of the world's top 12 producers, using a novel mixed-methods approach that integrates both qualitative and quantitative analyses to assess the feasibility of robotic pollination. The qualitative study identifies the benefits and challenges of current methods and explores how robotic pollination could address these challenges. The quantitative analysis explores the cost-effectiveness and practicality of implementing robotic pollination as a product and service. Findings indicate that most farmers use handheld pollination devices but face pollen wastage and application timing challenges. Economic analysis establishes a break-even point of & euro;685 per hectare for an annual single application, with a first robotic pollination of & euro;17 146 becoming cost-effective for orchards of at least 3.5 hectares and a second robotic solution of & euro;34 293 becoming cost-effective for orchards up to 7 hectares. A robotic pollination service priced at & euro;685 per hectare per application presents a low-risk and aviable alternative for growers. This study provides robust economic insights supporting the adoption of robotic pollination technologies. This study is crucial to make informed decisions to enhance kiwifruit production's productivity and sustainability through precise robotic-assisted pollination.

Abstract
Abstract This paper covers the state-of-the-art perception and control technologies in precision spraying and mowing in permanent crops. The search was performed in six different databases, resulting in 1849 publications, from which only 94 were considered for inclusion in this review. The analysis highlighted the importance of canopy characteristics in precision spraying, focusing on parameters like height, width, leaf area, and volume, primarily using LiDAR sensors. Vision sensors also complemented LiDAR-based approaches, with diverse applications such as fruit detection and disease diagnosis. Despite valuable knowledge from studies on spray coverage assessment and real-time smartphone analysis, challenges persist, including dynamic environmental factors and the different collector materials used. Moreover, the review considers the cost of Variable Rate Technology (VRT) solutions in agriculture, enhancing their impact on accessibility, adoption, and sustainability. While conventional herbicide-based weed management prevails, interest in alternative techniques like mechanical mowing and organic mulches is growing, promising improved soil health and reduced environmental impact, particularly in permanent crops. To address these challenges, agricultural robotics play a crucial role in automating precision spraying and mowing, optimizing resource usage, and increasing operational precision. This systematic review highlights the state of precision agriculture in permanent crops and emphasizes the need for continued research and development to improve the sustainability and efficiency of precision spraying and mowing systems in orchards, vineyards, and other woody crop environments.