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About

Raul Manuel Morais Pereira dos Santos graduated in Electrical Engineering (branch of Electronics, Instrumentation and Computer Science) at the University of Trás-Os-Montes e Alto Douro (UTAD), Portugal, in 1993. He obtained his Master's degree in Industrial Electronics from the University of Minho in 1998 and a PhD degree in Electrical and Computer Engineering (specialty microelectronics) obtained from UTAD in 2004. His aggregation in Electrical and Computer Engineering was obtained in UTAD in 2009. He is currently an Associate Professor with Habilitation at the Engineering Department of the School of Science and Technology of UTAD. His main areas of interest include sensors and sensor interfaces in CMOS microelectronics, energy harvesting techniques to power small and unattended electronic devices and wireless sensor networks in the context of agriculture/precision viticulture. Another area of interest is in the field of biomedical implantable devices, especially in biotelemetry systems regarding vibration microgenerators to produce electric energy inside smart prosthesis. He is currently an integrated member of the Institute of Integrated Systems and Computer Engineering of Porto (INESC-TEC).

Interest
Topics
Details

Details

003
Publications

2020

Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review

Authors
Mendes, J; Pinho, TM; dos Santos, FN; Sousa, JJ; Peres, E; Boaventura Cunha, J; Cunha, M; Morais, R;

Publication
Agronomy

Abstract
Traditionally farmers have used their perceptual sensorial systems to diagnose and monitor their crops health and needs. However, humans possess five basic perceptual systems with accuracy levels that can change from human to human which are largely dependent on the stress, experience, health and age. To overcome this problem, in the last decade, with the help of the emergence of smartphone technology, new agronomic applications were developed to reach better, cost-effective, more accurate and portable diagnosis systems. Conventional smartphones are equipped with several sensors that could be useful to support near real-time usual and advanced farming activities at a very low cost. Therefore, the development of agricultural applications based on smartphone devices has increased exponentially in the last years. However, the great potential offered by smartphone applications is still yet to be fully realized. Thus, this paper presents a literature review and an analysis of the characteristics of several mobile applications for use in smart/precision agriculture available on the market or developed at research level. This will contribute to provide to farmers an overview of the applications type that exist, what features they provide and a comparison between them. Also, this paper is an important resource to help researchers and applications developers to understand the limitations of existing tools and where new contributions can be performed.

2019

Localization Based on Natural Features Detector for Steep Slope Vineyards

Authors
Mendes, JM; dos Santos, FN; Ferraz, NA; do Couto, PM; dos Santos, RM;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
Placing ground robots to work in steep slope vineyards is a complex challenge. The Global Positioning System (GPS) signal is not always available and accurate. A reliable localization approach to detect natural features for this environment is required. This paper presents an improved version of a visual detector for Vineyards Trunks and Masts (ViTruDe) and, a robot able to cope pruning actions in steep slope vineyards (AgRob V16). In addition, it presents an augmented data-set for other localization and mapping algorithm benchmarks. ViTruDe accuracy is higher than 95% under our experiments. Under a simulated runtime test, the accuracy lies between 27% - 96% depending on ViTrude parametrization. This approach can feed a localization system to solve a GPS signal absence. The ViTruDe detector also considers economic constraints and allows to develop cost-effective robots. The augmented training and datasets are publicly available for future research work. © 2018 Springer Science+Business Media B.V., part of Springer Nature

2019

mySense: A comprehensive data management environment to improve precision agriculture practices

Authors
Morais, R; Silva, N; Mendes, J; Adao, T; Padua, L; Lopez Riquelme, J; Pavon Pulido, N; Sousa, JJ; Peres, E;

Publication
Computers and Electronics in Agriculture

Abstract

2019

Digital Ampelographer: A CNN Based Preliminary Approach

Authors
Adão, T; Pinho, TM; Ferreira, A; Sousa, AMR; Pádua, L; Sousa, J; Sousa, JJ; Peres, E; Morais, R;

Publication
Progress in Artificial Intelligence - Lecture Notes in Computer Science

Abstract

2019

Grapevine Varieties Classification Using Machine Learning

Authors
Marques, P; Pádua, L; Adão, T; Hruska, J; Sousa, J; Peres, E; Sousa, JJ; Morais, R; Sousa, AMR;

Publication
Progress in Artificial Intelligence - Lecture Notes in Computer Science

Abstract

Supervised
thesis

2019

Estudo e aplicação de modelos de previsão de doenças da vinha sobre plataformas de IOT

Author
Carlos Manuel Olo Peixoto

Institution
UTAD

2019

Contribuição para o estudo dos músculos da mastigação em modelo animal com doença degenerativa das articulações temporomandibulares

Author
Cláudia Sofia Barbosa da Costa Ribeiro

Institution
UP-FMDUP

2019

Self-adaptive electromagnetic energy harvesting system

Author
Pedro Miguel Rocha Carneiro

Institution
IES_Outra

2019

Sistemas de telecontagem para a monitorização do consumo de água

Author
Renato José Moura da Silva

Institution
UTAD

2019

Estimulação e sensoriamento da interface de implantes ósseos ativos instrumentados

Author
Nuno Miguel dos Santos Pinto da Silva

Institution
UTAD