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This service develops advanced solutions in automation and industrial robotics, including handlers and mobile robots, and promotes the integration of control intelligent systems and sensing.

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Browsing CRIIS by Author "5653"
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    Classification of an Agrosilvopastoral System Using RGB Imagery from an Unmanned Aerial Vehicle
    ( 2019) Guimarães,N ; Emanuel Peres Correia ; Marques,P ; Telmo Oliveira Adão ; Pádua,L ; Sousa,JJ ; Sousa,A ; 5653 ; 5490
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    Deep Learning-Based Methodological Approach for Vineyard Early Disease Detection Using Hyperspectral Data
    ( 2018) Hruska,J ; Morais,R ; Telmo Oliveira Adão ; Sousa,A ; Sousa,JJ ; Padua,L ; Marques,P ; Emanuel Peres Correia ; 5653 ; 5490
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    Digital Reconstitution of Road Traffic Accidents: A Flexible Methodology Relying on UAV Surveying and Complementary Strategies to Support Multiple Scenarios
    ( 2020) Luís Filipe Pádua ; Sousa,JJ ; António Ribeiro Sousa ; Emanuel Peres Correia ; Telmo Oliveira Adão ; Hruska,J ; Vanko,J ; Sousa,J ; 7802 ; 5490 ; 5653 ; 5844
    The reconstitution of road traffic accidents scenes is a contemporary and important issue, addressed both by private and public entities in different countries around the world. However, the task of collecting data on site is not generally focused on with the same orientation and relevance. Addressing this type of accident scenario requires a balance between two fundamental yet competing concerns: (1) information collecting, which is a thorough and lengthy process and (2) the need to allow traffic to flow again as quickly as possible. This technical note proposes a novel methodology that aims to support road traffic authorities/professionals in activities involving the collection of data/evidences of motor vehicle collision scenarios by exploring the potential of using low-cost, small-sized and light-weight unmanned aerial vehicles (UAV). A high number of experimental tests and evaluations were conducted in various working conditions and in cooperation with the Portuguese law enforcement authorities responsible for investigating road traffic accidents. The tests allowed for concluding that the proposed method gathers all the conditions to be adopted as a near future approach for reconstituting road traffic accidents and proved to be: faster, more rigorous and safer than the current manual methodologies used not only in Portugal but also in many countries worldwide.
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    Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery
    ( 2020) Luís Filipe Pádua ; Joaquim João Sousa ; Emanuel Peres Correia ; António Ribeiro Sousa ; Telmo Oliveira Adão ; Guimaraes,N ; 7802 ; 5490 ; 5653 ; 5844 ; 6354
    Unmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.
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    Estimation of Leaf Area Index in Chestnut Trees using Multispectral Data from an Unmanned Aerial Vehicle
    ( 2020) Martins,L ; Padua,L ; Emanuel Peres Correia ; Sousa,JJ ; Marques,P ; Sousa,A ; 5653
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    Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities
    ( 2020) Luís Filipe Pádua ; Emanuel Peres Correia ; Silva,N ; Marques,P ; Guimaraes,N ; Sousa,JJ ; 7802 ; 5653
    Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.
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    A full-stack model proposal to willingly implement E-learning at small universities: The University of Trás-Os-Montes E Alto Douro case
    ( 2020) Vaz,C ; Emanuel Peres Correia ; Sousa,J ; Reis,MJCS ; 5653
    This paper presents a model of a system capable of addressing the training needs identified for small universities, using the University of Trás-os-Montes e Alto Douro (UTAD) as a case study. In addition to supporting the typical needs of distance learning/education (e.g., e-learning), it is also intended that the proposed system complements the traditional classroom-based teaching. This model will have two modules: the physical/infrastructural module and the policies/practices module. While the physical module will have all the infrastructure services associated with educational practices, such as the e-learning platform, the policy module will include institutional policies and rules in the creation, development, practice and management of courses, equipment and physical spaces, such as exam rooms. In line with these, UTAD has come to recognize that e-learning should be part of its strategy for its training offer and, consequently, is being adopting new policies, namely through the signing of protocols with other institutions with more experience using e-learning. As such, a review of other models and systems that have been successfully implemented in other international reference universities will also be briefly presented here. The courses implemented so far, and the results achieved, are also presented and commented. © Italian e-Learning Association.
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    Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery
    ( 2020) Luís Filipe Pádua ; Sousa,JJ ; Emanuel Peres Correia ; António Ribeiro Sousa ; Telmo Oliveira Adão ; 5653 ; 5490 ; 7802 ; 5844
    The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.
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    Machine learning classification methods in hyperspectral data processing for agricultural applications
    ( 2018) Hruska,J ; Sousa,JJ ; Telmo Oliveira Adão ; Cunha,A ; Morais,R ; Pádua,L ; Marques,P ; Emanuel Peres Correia ; Sousa,AMR ; 5490 ; 5653
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    Mapping seaweed beds using multispectral imagery retrieved by unmanned aerial vehicles
    ( 2019) Azevedo,I ; Adão,T ; Pádua,L ; Borges,D ; Gonçalves,J ; Sousa Pinto,I ; Sousa,J ; Emanuel Peres Correia ; 5653
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    MixAR
    ( 2019) Pádua,L ; Agrellos,L ; Sousa,JJ ; Narciso,D ; Telmo Oliveira Adão ; Magalhães,L ; Emanuel Peres Correia ; 5653 ; 5490
    MixAR, a full-stack system capable of providing visualization of virtual reconstructions seamlessly integrated in the real scene (e.g. upon ruins), with the possibility of being freely explored by visitors, in situ, is presented in this article. In addition to its ability to operate with several tracking approaches to be able to deal with a wide variety of environmental conditions, MixAR system also implements an extended environment feature that provides visitors with an insight on surrounding points-of-interest for visitation during mixed reality experiences (positional rough tracking). A procedural modelling tool mainstreams augmentation models production. Tests carried out with participants to ascertain comfort, satisfaction and presence/immersion based on an in-field MR experience and respective results are also presented. Ease to adapt to the experience, desire to see the system in museums and a raised curiosity and motivation contributed as positive points for evaluation. In what regards to sickness and comfort, the lowest number of complaints seems to be satisfactory. Models' illumination/re-lightning must be addressed in the future to improve the user's engagement with the experiences provided by the MixAR system.
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    MixAR: A Multi-Tracking Mixed Reality System to Visualize Virtual Ancient Buildings Aligned Upon Ruins
    ( 2019) Adao,T ; Emanuel Peres Correia ; Agrellos,L ; Sousa,JJ ; Narciso,D ; Padua,L ; Magalhaes,L ; 5653
    MixAR, a full-stack system capable of providing visualization of virtual reconstructions seamlessly integrated in the real scene (e.g. upon ruins), with the possibility of being freely explored by visitors, in situ, is presented in this article. In addition to its ability to operate with several tracking approaches to be able to deal with a wide variety of environmental conditions, MixAR system also implements an extended environment feature that provides visitors with an insight on surrounding points-of-interest for visitation during mixed reality experiences (positional rough tracking). A procedural modelling tool mainstreams augmentation models production. Tests carried out with participants to ascertain comfort, satisfaction and presence/immersion based on an in-field MR experience and respective results are also presented. Ease to adapt to the experience, desire to see the system in museums and a raised curiosity and motivation contributed as positive points for evaluation. In what regards to sickness and comfort, the lowest number of complaints seems to be satisfactory. Models' illumination/re-lightning must be addressed in the future to improve the user's engagement with the experiences provided by the MixAR system.
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    MixAR: A Multi-Tracking Mixed Reality System to Visualize Virtual Ancient Buildings Aligned Upon Ruins
    ( 2019) Telmo Oliveira Adão ; Emanuel Peres Correia ; Agrellos,L ; Sousa,JJ ; Narciso,D ; Luís Filipe Pádua ; Magalhães,L ; 7802 ; 5653 ; 5490
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    Monitoring of Chestnut Trees Using Machine Learning Techniques Applied to UAV-Based Multispectral Data
    ( 2020) Martins,L ; Sousa,JJ ; Emanuel Peres Correia ; Luís Filipe Pádua ; Marques,P ; António Ribeiro Sousa ; 5844 ; 7802 ; 5653
    Phytosanitary conditions can hamper the normal development of trees and significantly impact their yield. The phytosanitary condition of chestnut stands is usually evaluated by sampling trees followed by a statistical extrapolation process, making it a challenging task, as it is labor-intensive and requires skill. In this study, a novel methodology that enables multi-temporal analysis of chestnut stands using multispectral imagery acquired from unmanned aerial vehicles is presented. Data were collected in different flight campaigns along with field surveys to identify the phytosanitary issues affecting each tree. A random forest classifier was trained with sections of each tree crown using vegetation indices and spectral bands. These were first categorized into two classes: (i) absence or (ii) presence of phytosanitary issues. Subsequently, the class with phytosanitary issues was used to identify and classify either biotic or abiotic factors. The comparison between the classification results, obtained by the presented methodology, with ground-truth data, allowed us to conclude that phytosanitary problems were detected with an accuracy rate between 86% and 91%. As for determining the specific phytosanitary issue, rates between 80% and 85% were achieved. Higher accuracy rates were attained in the last flight campaigns, the stage when symptoms are more prevalent. The proposed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Moreover, it is also able to identify decline or expansion situations. It may be of help as part of decision support systems that further improve on the efficient and sustainable management practices of chestnut stands.
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    MULTI-PURPOSE CHESTNUT CLUSTERS DETECTION USING DEEP LEARNING: A PRELIMINARY APPROACH
    ( 2019) Adão,T ; Pádua,L ; Pinho,TM ; Hruška,J ; Sousa,A ; Sousa,JJ ; Morais,R ; Emanuel Peres Correia ; 5653
    <p><strong>Abstract.</strong> In the early 1980's, the European chestnut tree (<i>Castanea sativa, Mill.</i>) assumed an important role in the Portuguese economy. Currently, the Trás-os-Montes region (Northeast of Portugal) concentrates the highest chestnuts production in Portugal, representing the major source of income in the region (€50M-€60M).</p> <p>The recognition of the quality of the Portuguese chestnut varieties has increasing the international demand for both industry and consumer-grade segments. As result, chestnut cultivation intensification has been witnessed, in such a way that widely disseminated monoculture practices are currently increasing environmental disaster risks. Depending on the dynamics of the location of interest, monocultures may lead to desertification and soil degradation even if it encompasses multiple causes and a whole range of consequences or impacts. In Trás-os-Montes, despite the strong increase in the cultivation area, phytosanitary problems, such as the chestnut ink disease (<i>Phytophthora cinnamomi</i>) and the chestnut blight (<i>Cryphonectria parasitica</i>), along with other threats, e.g. chestnut gall wasp (<i>Dryocosmus kuriphilus</i>) and nutritional deficiencies, are responsible for a significant decline of chestnut trees, with a real impact on production. The intensification of inappropriate agricultural practices also favours the onset of phytosanitary problems. Moreover, chestnut trees management and monitoring generally rely on in-field time-consuming and laborious observation campaigns. To mitigate the associated risks, it is crucial to establish an effective management and monitoring process to ensure crop cultivation sustainability, preventing at the same time risks of desertification and land degradation.</p> <p>Therefore, this study presents an automatic method that allows to perform chestnut clusters identification, a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the chestnut fruits, considering a small dataset acquired based on digital terrestrial camera.</p>
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    Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery
    ( 2018) Marques,P ; Pádua,L ; Morais,R ; Hruška,J ; Sousa,J ; Telmo Oliveira Adão ; Emanuel Peres Correia ; 5490 ; 5653
    This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.
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    Mysense-Webgis: A Graphical Map Layering-Based Decision Support Tool for Agriculture
    ( 2020) Morais,R ; Emanuel Peres Correia ; Adao,T ; Soares,A ; Padua,L ; Guimardes,N ; Pinho,T ; Sousa,JJ ; 5653
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    mySense: A comprehensive data management environment to improve precision agriculture practices
    ( 2019) Telmo Oliveira Adão ; Emanuel Peres Correia ; Sousa,JJ ; Pavon Pulido,N ; Lopez Riquelme,J ; Padua,L ; Morais,R ; Silva,N ; Mendes,J ; 5653 ; 5490
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    A pilot digital image processing approach for detecting vineyard parcels in Douro region through high-resolution aerial imagery
    ( 2018) Telmo Oliveira Adão ; Pádua,L ; Sousa,JJ ; Hruska,J ; Marques,P ; Emanuel Peres Correia ; 5653 ; 5490