The images were taken in vineyards owned by Bodegas Terras Gauda in September 2021.-- The camera used was a digital single-lens reflex camera, Canon EOS 7D. The camera was placed on-board a wheeled robot. Images were automatically taken at a speed of almost 1 fps, during the movement of the robot.-- The weather conditions when the images were collected were sunny., Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA)., Set of images of vines affected by late stage Botritys., We acknowledge received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017111, FlexiGroBots project., Peer reviewed

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production and cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles and standardizing processes across platforms to maximize interoperability. The lack of drone technology standardisation, communication barriers, high costs, and post-processing requirements sometimes hinder their widespread use in agriculture. This research introduces a standardized data fusion framework for creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) for Site-Specific Crop Management (SSM). Two spatial interpolation methods were used (Inverse Distance Weight, IDW, and Triangulated Irregular Networks, TIN), selected for their computational efficiency and input flexibility. The proposed framework can use different UAV image sources and offers versatility, speed, and efficiency, consuming up to 98 % less time, energy, and computing requirements than standard photogrammetry techniques, providing rapid field information, allowing edge computing incorporation into the UAV data acquisition phase. Experiments conducted in Spain, Serbia, and Finland in 2022 under the H2020 FlexiGroBots project demonstrated a strong correlation between results from this method and those from standard photogrammetry techniques (up to r = 0.93). In addition, the correlation with Sentinel 2 satellite images was as strong as that obtained with photogrammetry-based orthomosaics (up to r = 0.8). The proposed approach could support irrigation leak detection, soil parameter estimation, weed management, and satellite integration for agriculture., This work has been carried out in the scope of the H2020 FlexiGroBots project, which the European Commission has funded in the scope of its H2020 program (contract number 101017111, https://flexigrobots-h2020.eu/). Dr. Sergio Vélez's postdoctoral fellowship contract has been supported by the Iberdrola Foundation and the European Commission under the Marie Skłodowska-Curie Actions (MSCA)—E4F, part of the Horizon 2020 program (Grant Agreement No 101034297, https://doi.org/10.3030/101034297). The authors acknowledge valuable help and contributions from 'Bodegas Terras Gauda, S.A.' and all project partners., Peer reviewed

The increasing demand for agricultural products and rising production costs have intensified labour shortages in the agricultural sector. Manual harvesting remains essential for products with specific designations, such as wine grapes, where automated solutions cannot match human operators’ dexterity, speed, and care. Minimizing transportation time is also crucial for preserving produce quality and optimizing efficiency. This study aims to optimize harvesting efficiency and vineyard management through the design and implementation of a mobile robotic platform. The platform combines operator dexterity with robotic assistance, continuously tracking operators as they deposit harvested grapes into a harvesting box carried by a robot while gathering data for yield map development. Adaptable to various manual fruit-picking processes, the platform can be integrated into a collaborative harvesting assistance fleet. Field experiments conducted at the Bodegas Terras Gauda (UTM coordinates: 41.95, −8.80, O Rosal, Pontevedra, Spain) vineyard, indicated that operators using robotic assistance reduced their average harvesting time per box by 6 min, increased their total harvested yield by 72.50 kg after two hours (up to 50% more), and reduced manual labour costs by 22.50%. A yield map was developed with high-accuracy GNSS data and an industrial scale mounted on the robot. The map geolocates the weights collected with a maximum variability error of 0.11 kg and successfully expresses grapevine density variability within the same vineyard row. The system preserves produce quality during transportation and significantly eliminates physical strain among operators. These results demonstrate the potential of the robotic platform to improve the efficiency of manual harvesting while maintaining high-quality outcomes., This paper was carried out within the framework of the FlexiGroBots project funded by the European Union under the H2020 Program with Grant Agreement Id. 101017111. The authors acknowledge valuable help and contributions from Bodegas Terras Gauda, S.A. for generously providing the land to conduct the experiments and all partners of the project. All authors have reviewed and approved the final manuscript., Peer reviewed

This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring, [Data Availability Statement] The datasets can be found at [42,43]. The open-source software can be
found at https://github.com/druzzo/IrrigationEcolabel (accessed on 26 September 2025). The video
tutorial can be found at https://github.com/druzzo/IrrigationEcolabel?tab=readme-ov-file#videotutorial (accessed on 26 September 2025)., Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems., This work has been carried out in the scope of the H2020 FlexiGroBots project, which has been funded by the European Commission in the scope of its H2020 program (contract number 101017111, https://flexigrobots-h2020.eu/ (accessed on 26 September 2025))., Peer reviewed

Viticulture benefits significantly from rapid grape bunch identification and counting, enhancing yield and quality. Recent technological and machine learning advancements, particularly in deep learning, have provided the tools necessary to create more efficient, automated processes that significantly reduce the time and effort required for these tasks. On one hand, drone, or Unmanned Aerial Vehicles (UAV) imagery combined with deep learning algorithms has revolutionised agriculture by automating plant health classification, disease identification, and fruit detection. However, these advancements often remain inaccessible to farmers due to their reliance on specialized hardware like ground robots or UAVs. On the other hand, most farmers have access to smartphones. This article proposes a novel approach combining UAVs and smartphone technologies. An AI-based framework is introduced, integrating a 5-stage AI pipeline combining object detection and pixel-level segmentation algorithms to automatically detect grape bunches in smartphone images of a commercial vineyard with vertical trellis training. By leveraging UAV-captured data for training, the proposed model not only accelerates the detection process but also enhances the accuracy and adaptability of grape bunch detection across different devices, surpassing the efficiency of traditional and purely UAV-based methods. To this end, using a dataset of UAV videos recorded during early growth stages in July (BBCH77-BBCH79), the X-Decoder segments vegetation in the front of the frames from their background and surroundings. X-Decoder is particularly advantageous because it can be seamlessly integrated into the AI pipeline without requiring changes to how data is captured, making it more versatile than other methods. Then, YOLO is trained using the videos and further applied to images taken by farmers with common smartphones (Xiaomi Poco X3 Pro and iPhone X). In addition, a web app was developed to connect the system with mobile technology easily. The proposed approach achieved a precision of 0.92 and recall of 0.735, with an F1 score of 0.82 and an Average Precision (AP) of 0.802 under different operation conditions, indicating high accuracy and reliability in detecting grape bunches. In addition, the AI-detected grape bunches were compared with the actual ground truth, achieving an R2 value as high as 0.84, showing the robustness of the system. This study highlights the potential of using smartphone imaging and web applications together, making an effort to integrate these models into a real platform for farmers, offering a practical, affordable, accessible, and scalable solution. While smartphone-based image collection for model training is labour-intensive and costly, incorporating UAV data accelerates the process, facilitating the creation of models that generalise across diverse data sources and platforms. This blend of UAV efficiency and smartphone precision significantly cuts vineyard monitoring time and effort., This work has been carried out in the scope of the H2020 FlexiGroBots project, which has been funded by the European Commission in the scope of its H2020 programme (contract number 101017111, https://flexigrobots-h2020.eu/). The authors acknowledge valuable help and contributions from Emilio Rodríguez and Pablo Fernández, from "Bodegas Terras Gauda, S.A.". Dr. Sergio Vélez’s Distinguished Researcher contract (Beatriz Galindo BG23/00073) is funded by the Spanish Ministry of Science, Innovation and Universities under the 2023 call, within the framework of the State Plan for Scientific, Technical, and Innovation Research (PEICTI) 2021-2023, Peer reviewed

Effective crop monitoring is vital for optimizing agricultural production, improving crop quality, and reducing
operational costs. Ground-based robotic platforms have emerged as precise alternatives to manual inspections,
enabling detailed and organized data collection. However, many small and medium-sized agricultural robots are
limited in terms of autonomy and reliability. This paper presents the full automation of a low-cost commercial
electric vehicle, the Renault Twizy, for use as an autonomous inspection platform in agricultural environments.
The system integrates fuzzy logic controllers and a modular CAN-based control architecture to automate the
steering, throttle, and braking subsystems. Autonomous navigation capabilities are achieved by integrating the
ROS Navigation Stack. Field experiments conducted in vineyard environments validated the system’s ability to
navigate autonomously at low speeds, maintain stable trajectories, and perform inter-row manoeuvres without
human intervention. The results demonstrate the feasibility and scalability of repurposing urban electric vehicles
for agricultural robotics, offering enhanced robustness, extended operational autonomy, and reduced development costs compared with those of conventional robotic platforms., This paper was carried out within the framework of the FlexiGroBots project, funded by the European Union under the H2020 Program with Grant Agreement Id. 101017111, the PIE-CSIC project “Artificial intelligence and collaborative robotics in precision agriculture” (AGROBOTS, 202450E209), and the iRoboCity2030-CM project (TEC-2024/TEC-62), funded by the Comunidad de Madrid., Peer reviewed

Extracting phenotypic traits of grape bunch is crucial for accurately monitoring grape quality, health, and yield estimation. This is important for optimising resources, enhancing marketing strategies, and boosting overall agricultural productivity. While most research concentrates on data processing algorithms, this study focused on the preceding step: collecting reliable data. Object detection and tracking enable precise monitoring and quantification of fruit, facilitating agricultural management. This study compares two data acquisition methodologies for grape bunch detection and tracking in a commercial vineyard where leaf removal was not performed: a traditional single-view approach and a multiple-viewing method designed to mitigate fruit occlusion issues. The PointTrack algorithm, trained and validated using MOTS annotations, was employed to evaluate detection and tracking performance through metrics of three trials. The multiple-view method achieved i) higher ratio between tracked and GT detections of 74 % compared to 23 % for the single-view approach and ii) enhanced tracking metrics, with the multiple viewing trials metrics ranging from −1.35 to 3.84 for MOTSA (Multiple Object Tracking and Segmentation Accuracy) and sMOTSA (soft MOTSA), and iii) higher correlation and lower RMSE of grape bunch phenotypic traits (OIV codes 202 and 203) compared to ground truth measurements (R2 = 0.53, RMSE = 19.13). Nonetheless, the multi-view technique was compromised by motion blur due to UAV movements, complicating the tracking process. This study underscores the importance of strategic data acquisition in improving performance for fruit detection and tracking. Future work should extend this methodology to other fruit varieties and environments to validate its broader applicability, enhancing the reliability of yield estimation in precision agriculture., This work has been carried out in the scope of the H2020 FlexiGroBots project, which has been funded by the European Commission in the scope of its H2020 programme (contract number 101017111, https://flexigrobots-h2020.eu/). The authors acknowledge valuable help and contributions from 'Bodegas Terras Gauda, S.A.' and all partners of the project. This work has also been developed in the scope of the H2020 Icaerus project (contract number 101060643, https://icaerus.eu/). Dr Sergio Vélez contract has been supported partially by the Iberdrola Foundation and the European Commission under the Marie Skłodowska-Curie Actions (MSCA) - E4F, part of the Horizon 2020 program (Grant Agreement No 101034297, https://doi.org/10.3030/101034297). We extend our gratitude to the Horizon Smart Droplets project (contract number 101070496, https://smartdroplets.eu/)., Peer reviewed

SLAM (Simultaneous Localization and Mapping) is an efficient method for robot to percept surrendings and make decisions, especially for robots in agricultural scenarios. Perception and path planning in an automatic way is crucial for precision agriculture. However, there are limited public datasets to implement and develop robotic algorithms for agricultural environments. Therefore, we collected dataset “GrapeSLAM”. The ‘‘GrapeSLAM'' dataset comprises video data collected from vineyards to support agricultural robotics research. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing videos under different illumination conditions, and (2) trajectories of the UAV during each flight collected by RTK and IMU. The UAV used was Phantom 4 RTK, equipped with a high resolution camera, flying at around 1 to 3 meters above ground level., The authors acknowledge the support of the Wageningen University, Chinese Academy of Agricultureal Sciences joint Ph.D. program. This work has also been carried out in the scope of the H2020 FlexiGroBots project, which the European Commission has funded in its H2020 program (contract number 101017111, https://flexigrobots-h2020.eu/ ). The authors acknowledge valuable help and contributions from Emilio Rodríguez and Pablo Fernández, from “Bodegas Ter- ras Gauda, S.A.”, Peer reviewed

Este trabajo presenta un sistema multi-robot heterogéneo para la detección y el tratamiento localizado de enfermedades agrícolas. Está compuesto por dos robots de inspección, basados en vehículos eléctricos comerciales, y dos robots de tratamiento con pulverizadores eléctricos. Cada robot cuenta con cámaras estéreo y receptores GNSS RTK, todos ellos coordinados por un sistema de gestión de misiones en la nube. La estrategia separa las tareas de inspección y tratamiento, permitiendo navegación autónoma, detección en tiempo real mediante redes neuronales convolucionales (YOLOv8) y aplicación localizada del
tratamiento. El sistema fue validado en un viñedo comercial afectado por Botrytis cinerea, logrando cobertura continua, solapamiento medio del 20 % y una precisión del 95 %, con un 30 % de falsos positivos. Los tratamientos se aplicaron solo en zonas infectadas. Esta solución ofrece una alternativa precisa, escalable y eficiente para la agricultura de precisión, mejorando la sostenibilidad mediante el uso racional de recursos y la automatización colaborativa., Este trabajo se ha realizado en el marco del proyecto FlexiGroBots, financiado por la Unión Europea, Programa
H2020, con el acuerdo de subvención n.º 101017111., Peer reviewed

This paper presents a multi-robot system for the autonomous detection and treatment of pests. The
system comprises two inspection robots, based on commercial electric vehicles, and two treatment
robots equipped with electric sprayers. All four robots are fitted with cameras and global navigation
satellite system (GNSS) receivers and operate under a cloud-based mission management system.
The robots function decoupled, enabling autonomous navigation, real-time pest detection using
convolutional neural network (CNN) models, and precision treatment application. The system was
validated in Botrytis detection in white grape vineyards, demonstrating accurate path following,
continuous image acquisition with an average 20 % overlap, and 95 % detection accuracy with a
30 % false positive rate., This work has been funded by the EU FlexiGroBots project (https://cordis.europa.eu/project/ id/101017111), Peer reviewed

Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems.