Abstract

Abstract
Pruning consists on an agricultural trimming procedure that is crucial in some species of plants to promote healthy growth and increased yield. Generally, this task is done through manual labour, which is costly, physically demanding, and potentially dangerous for the worker. Robotic pruning is an automated alternative approach to manual labour on this task. This approach focuses on selective pruning and requires the existence of an end-effector capable of detecting and cutting the correct point on the branch to achieve efficient pruning. This paper reviews and analyses different end-effectors used in robotic pruning, which helped to understand the advantages and limitations of the different techniques used and, subsequently, clarified the work required to enable autonomous pruning.

Abstract
In nature, and particularly in agriculture, pollination is fundamental for the sustainability of our society. In this context, pollination is a vital process underlying crop yield quality and is responsible for the biodiversity and the standards of the flora. Bees play a crucial role in natural pollination; however, their populations are declining. Robots can help maintain pollination levels while humans work to recover bee populations. Swarm robotics approaches appear promising for robotic pollination. This paper proposes the cooperation between multiple Unmanned Aerial Vehicles (UAVs) and an Unmanned Ground Vehicle (UGV), leveraging the advantages of collaborative work for pollination, referred to as Pollinationbots. Pollinationbots is based in swarm behaviors and methodologies to implement more effective pollination strategies, ensuring efficient pollination across various scenarios. The paper presents the architecture of the Pollinationbots system, which was evaluated using the Webots simulator, focusing on path planning and follower behavior. Preliminary simulation results indicate that this is a viable solution for robotic pollination. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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
This study demonstrates the application of a tomography-like (TL) method to monitor grape maturation dynamics over two growing seasons (2021-2022) in the Douro Wine Region. Using a Vis-NIR point-of-measurement sensor, which employs visible and near-infrared light to penetrate grape tissues non-destructively and provide spectral data to predict internal composition, this approach captures non-destructive measurements of key physicochemical properties, including soluble solids content (SSC), weight-to-volume ratio, chlorophyll and anthocyanin levels across internal grape tissues-skin, pulp, and seeds-over six post-veraison stages. The collected data were used to generate detailed metabolic maps of maturation, integrating topographical factors such as altitude and NDVI-based (normalised difference vegetation index) vigour assessments, which revealed significant (p < 0.05) variations in SSC, chlorophyll, and anthocyanin levels across vineyard zones. The metabolic maps generated from the TL method enable high-throughput data to reveal the impact of environmental variability on grape maturation across distinct vineyard areas. Predictive models using random forest (RF) and self-learning artificial intelligence (SL-AI) algorithms showed RF's robustness, achieving stable predictions with R-2 >= 0.86 and MAPE <= 33.83 %. To illustrate the TL method's practical value, three hypothetical decision models were developed for targeted winemaking objectives based on SSC, chlorophyll in the pulp, and anthocyanin in the skin and seeds. These models underscore the TL method's ability to support site-specific management (SSM) by providing actionable agricultural practices (e.g. harvest) into vineyard management, guiding winemakers to implement tailored interventions based on metabolic profiles rather than only cultivar characteristics. This precision viticulture (PV) approach enhances wine quality and production efficiency by aligning vineyard practices with specific wine quality goals.