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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

2021

Potential Non-Invasive Technique for Accessing Plant Water Contents Using a Radar System

Authors
Santos, LC; dos Santos, FN; Morais, R; Duarte, C;

Publication
AGRONOMY-BASEL

Abstract
Sap flow measurements of trees are today the most common method to determine evapotranspiration at the tree and the forest/crop canopy level. They provide independent measurements for flux comparisons and model validation. The most common approach to measure the sap flow is based on intrusive solutions with heaters and thermal sensors. This sap flow sensor technology is not very reliable for more than one season crop; it is intrusive and not adequate for low diameter trunk trees. The non-invasive methods comprise mostly Radio-frequency (RF) technologies, typically using satellite or air-born sources. This system can monitor large fields but cannot measure sap levels of a single plant (precision agriculture). This article studies the hypothesis to use of RF signals attenuation principle to detect variations in the quantity of water present in a single plant. This article presents a well-defined experience to measure water content in leaves, by means of high gains RF antennas, spectrometer, and a robotic arm. Moreover, a similar concept is studied with an off-the-shelf radar solution—for the automotive industry—to detect changes in the water presence in a single plant and leaf. The conclusions indicate a novel potential application of this technology to precision agriculture as the experiments data is directly related to the sap flow variations in plant.

2021

A Versatile, Low-Power and Low-Cost IoT Device for Field Data Gathering in Precision Agriculture Practices

Authors
Morais, R; Mendes, J; Silva, R; Silva, N; Sousa, JJ; Peres, E;

Publication
AGRICULTURE-BASEL

Abstract
Spatial and temporal variability characterization in Precision Agriculture (PA) practices is often accomplished by proximity data gathering devices, which acquire data from a wide variety of sensors installed within the vicinity of crops. Proximity data acquisition usually depends on a hardware solution to which some sensors can be coupled, managed by a software that may (or may not) store, process and send acquired data to a back-end using some communication protocol. The sheer number of both proprietary and open hardware solutions, together with the diversity and characteristics of available sensors, is enough to deem the task of designing a data acquisition device complex. Factoring in the harsh operational context, the multiple DIY solutions presented by an active online community, available in-field power approaches and the different communication protocols, each proximity monitoring solution can be regarded as singular. Data acquisition devices should be increasingly flexible, not only by supporting a large number of heterogeneous sensors, but also by being able to resort to different communication protocols, depending on both the operational and functional contexts in which they are deployed. Furthermore, these small and unattended devices need to be sufficiently robust and cost-effective to allow greater in-field measurement granularity 365 days/year. This paper presents a low-cost, flexible and robust data acquisition device that can be deployed in different operational contexts, as it also supports three different communication technologies: IEEE 802.15.4/ZigBee, LoRa/LoRaWAN and GRPS. Software and hardware features, suitable for using heat pulse methods to measure sap flow, leaf wetness sensors and others are embedded. Its power consumption is of only 83 µA during sleep mode and the cost of the basic unit was kept below the EUR 100 limit. In-field continuous evaluation over the past three years prove that the proposed solution—SPWAS’21—is not only reliable but also represents a robust and low-cost data acquisition device capable of gathering different parameters of interest in PA practices.

2021

Grapevine Variety Identification Through Grapevine Leaf Images Acquired in Natural Environment

Authors
Carneiro, GS; Pádua, L; Sousa, JJ; Peres, E; Morais, R; Cunha, A;

Publication
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium, July 11-16, 2021

Abstract

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-BASEL

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.

2020

Utilization of Bioelectrical Impedance to Predict Intramuscular Fat and Physicochemical Traits of the Beef Longissimus Thoracis et Lumborum Muscle

Authors
Afonso, J; Guedes, C; Santos, V; Morais, R; Silva, J; Teixeira, A; Silva, S;

Publication
FOODS

Abstract
The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% <= CV <= 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (-0.50 <= r <= 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Supervised
thesis

2021

Estimulação e sensoriamento da interface de implantes ósseos ativos instrumentados

Author
Nuno Miguel dos Santos Pinto da Silva

Institution
UTAD

2021

Automatic detection of earthquake's deformations in SAR interferograms

Author
Bruno Miguel Ferreira Silva

Institution

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

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

Author
Carlos Manuel Olo Peixoto

Institution
UTAD

2019

Self-adaptive electromagnetic energy harvesting system

Author
Pedro Miguel Rocha Carneiro

Institution
IES_Outra