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About

About

Emanuel Soares Peres Correia is an assistant professor at Trás-os-Montes e Alto Douro University (UTAD) in Portugal. He has been lecturing in areas such as web development, electronics, power electronics, telecommunications and computer networks since 2003. He is an integrated researcher at the Centre for Robotics in Industry and Intelligent Systems (CRIIS) in Technology and Science Associate Laboratory (INESC-TEC) and his research focuses on combining sensing networks, in-field processing units and mesh communication networks to develop data acquisition systems which enable decision support tools for precision agriculture. Human–Machine interaction, namely augmented reality systems and new interfaces, in areas such as education, agriculture and tourism, is also an area of interest. His research has been presented at international conferences such as CENTERIS, EDUCON, CISTI, CSEDU and SPIE and he has been published in the e.g. Journal of Computers and Electronics in Agriculture, Journal of Remote Sensing, International Journal of Remote Sensing, Journal of Theoretical and Applied Electronic Commerce Research, Journal of Applied Logic and Procedia Technology. He is an Editor-in-Chief of the International Journal of Web Portals (IJWP) since November 2016. As a member of UTAD’s Urban Eco-Efficiency Unit, he is currently involved in several research projects mainly related with the application of UAV in agriculture and forestry.

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Topics
Details

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Publications

2022

Vineyard classification using OBIA on UAV-based RGB and multispectral data: A case study in different wine regions

Authors
Padua, L; Matese, A; Di Gennaro, SF; Morais, R; Peres, E; Sousa, JJ;

Publication
COMPUTERS AND ELECTRONICS IN AGRICULTURE

Abstract

2022

CENTERIS 2021 - International Conference on ENTERprise Information Systems / ProjMAN 2021 - International Conference on Project MANagement / HCist 2021 - International Conference on Health and Social Care Information Systems and Technologies 2021, Braga, Portugal

Authors
Cruz Cunha, MM; Martinho, R; Rijo, R; Domingos, D; Peres, E;

Publication
CENTERIS/ProjMAN/HCist

Abstract

2022

VineInspector: The Vineyard Assistant

Authors
Mendes, J; Peres, E; dos Santos, FN; Silva, N; Silva, R; Sousa, JJ; Cortez, I; Morais, R;

Publication
AGRICULTURE-BASEL

Abstract
Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported on gained experience to assess aspects of plants’ phenological development, as well as indicators relating to the onset of common plagues and diseases. Aiming to mimic in-field direct observation, this paper presents VineInspector: a low-cost, self-contained and easy-to-install system, which is able to measure microclimatic parameters, and also to acquire images using multiple cameras. It is built upon a stake structure, rendering it suitable for deployment across a vineyard. The approach through which distinguishable attributes are detected, classified and tallied in the periodically acquired images, makes use of artificial intelligence approaches. Furthermore, it is made available through an IoT cloud-based support system. VineInspector was field-tested under real operating conditions to assess not only the robustness and the operating functionality of the hardware solution, but also the AI approaches’ accuracy. Two applications were developed to evaluate VineInspector’s consistency while a viticulturist’ assistant in everyday practices. One was intended to determine the size of the very first grapevines’ shoots, one of the required parameters of the well known 3–10 rule to predict primary downy mildew infection. The other was developed to tally grapevine moth males captured in sex traps. Results show that VineInspector is a logical step in smart proximity monitoring by mimicking direct visual observation from experienced viticulturists. While the latter traditionally are responsible for a set of everyday practices in the field, these are time and resource consuming. VineInspector was proven to be effective in two of these practices, performing them automatically. Therefore, it enables both the continuous monitoring and assessment of a vineyard’s phenological development in a more efficient manner, making way to more assertive and timely practices against pests and diseases.

2022

UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications

Authors
Sousa, JJ; Toscano, P; Matese, A; Di Gennaro, SF; Berton, A; Gatti, M; Poni, S; Padua, L; Hruska, J; Morais, R; Peres, E;

Publication
SENSORS

Abstract
Hyperspectral aerial imagery is becoming increasingly available due to both technology evolution and a somewhat affordable price tag. However, selecting a proper UAV + hyperspectral sensor combo to use in specific contexts is still challenging and lacks proper documental support. While selecting an UAV is more straightforward as it mostly relates with sensor compatibility, autonomy, reliability and cost, a hyperspectral sensor has much more to be considered. This note provides an assessment of two hyperspectral sensors (push-broom and snapshot) regarding practicality and suitability, within a precision viticulture context. The aim is to provide researchers, agronomists, winegrowers and UAV pilots with dependable data collection protocols and methods, enabling them to achieve faster processing techniques and helping to integrate multiple data sources. Furthermore, both the benefits and drawbacks of using each technology within a precision viticulture context are also highlighted. Hyperspectral sensors, UAVs, flight operations, and the processing methodology for each imaging type’ datasets are presented through a qualitative and quantitative analysis. For this purpose, four vineyards in two countries were selected as case studies. This supports the extrapolation of both advantages and issues related with the two types of hyperspectral sensors used, in different contexts. Sensors’ performance was compared through the evaluation of field operations complexity, processing time and qualitative accuracy of the results, namely the quality of the generated hyperspectral mosaics. The results shown an overall excellent geometrical quality, with no distortions or overlapping faults for both technologies, using the proposed mosaicking process and reconstruction. By resorting to the multi-site assessment, the qualitative and quantitative exchange of information throughout the UAV hyperspectral community is facilitated. In addition, all the major benefits and drawbacks of each hyperspectral sensor regarding its operation and data features are identified. Lastly, the operational complexity in the context of precision agriculture is also presented.

2022

Segmentation as a Preprocessing Tool for Automatic Grapevine Classification

Authors
Carneiro, GA; Padua, L; Peres, E; Morais, R; Sousa, JJ; Cunha, A;

Publication
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

Abstract

Supervised
thesis

2021

Tecnologias Digitais na Educação Básica - A utilização de TIC para fins educativos e o seu impacto na performance dos estudantes

Author
João Pedro Soares Coelho da Silva

Institution
UP-FEP

2020

Elearning implementation at small universities

Author
Carlos Manuel Rodrigues Soares Vaz

Institution
UTAD

2020

Hyperspectral data analysis for agriculture applications

Author
Jonas Hruska

Institution
UTAD

2020

Sistemas cognitivos de interpretação inteligente em contexto agro-florestal

Author
Miguel Moreira da Silva Lima Barbosa

Institution
UP-FCUP

2020

Automatic analysis of UAS-based multi-temporal data as support to a precision agroforestry management system

Author
Luís Filipe Machado Pádua

Institution
UTAD