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About

Filipe Neves dos Santos was born in São Paio de Oleiros, Portugal, in 1979. He olds a Licenciatura (5-year degree) in Electrical and Computer Engineering in 2003 from Instituto Superior de Engenharia do Porto (ISEP), a M.Sc. in Electrical and Computer Engineering from the Instituto Superior Técnico (IST) da Universidade Técnica de Lisboa, in 2007, and received the PhD degree in Electrical and Computer Engineering at the Faculdade de Engenharia (FEUP), Universidade do Porto, Portugal, in 2014. His professional passion is to develop autonomous robots and machinery to solve real problems, desires and needs of our society and to contribute for self-sustainability and fairness of the global economy. Actually, He is focused in developing and researching robotic solutions for agriculture and forestry sector, where is required a higher efficiency for our world self-sustainability. Considering his closer regional reality, he have setup the goal to promote agricultural robotic based projects and develop robots that can operate fully autonomously and safely in steep-slope scenarios, which is a common reality of North of Portugal and in other large number of world regions. As so, he is interested in explore and develop robots for specific agricultural and forestall tasks such as: monitoring (by ground), spraying, logistics, pruning, and selective harvesting. The successfully execution of these task is largely dependent on the robustness of specific robotic systems, such as: - Visual Perception; - Navigation (localization, mapping and path planning); and - Manipulation and end tools. For that reason Visual Perception and Navigation are his main research fields inside of robotics research. His formation in Electronics and Computer Engineer (Bachelor (old-one of 5 years) MSc (sensor fusion), PhD (semantic mapping) ), experience of 4 years as entrepreneur (technological startup), 8 year as robotics researcher, 5 years as manager (in supporting tasks in a family enterprise), and 6 year as electronics technician will help him to successfully contribute for the agricultural and forestall robotics future.

Interest
Topics
Details

Details

023
Publications

2021

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Authors
de Aguiar, ASP; dos Santos, FN; Placido Sobreira, HM; Cunha, JB; de Sousa, AJM;

Publication
Robotics and Autonomous Systems

Abstract

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

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

Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection

Authors
Aguiar, AS; Monteiro, NN; dos Santos, FN; Pires, EJS; Silva, D; Sousa, AJ; Boaventura Cunha, J;

Publication
Agriculture

Abstract
The development of robotic solutions in unstructured environments brings several challenges, mainly in developing safe and reliable navigation solutions. Agricultural environments are particularly unstructured and, therefore, challenging to the implementation of robotics. An example of this is the mountain vineyards, built-in steep slope hills, which are characterized by satellite signal blockage, terrain irregularities, harsh ground inclinations, and others. All of these factors impose the implementation of precise and reliable navigation algorithms, so that robots can operate safely. This work proposes the detection of semantic natural landmarks that are to be used in Simultaneous Localization and Mapping algorithms. Thus, Deep Learning models were trained and deployed to detect vine trunks. As significant contributions, we made available a novel vine trunk dataset, called VineSet, which was constituted by more than 9000 images and respective annotations for each trunk. VineSet was used to train state-of-the-art Single Shot Multibox Detector models. Additionally, we deployed these models in an Edge-AI fashion and achieve high frame rate execution. Finally, an assisted annotation tool was proposed to make the process of dataset building easier and improve models incrementally. The experiments show that our trained models can detect trunks with an Average Precision up to 84.16% and our assisted annotation tool facilitates the annotation process, even in other areas of agriculture, such as orchards and forests. Additional experiments were performed, where the impact of the amount of training data and the comparison between using Transfer Learning and training from scratch were evaluated. In these cases, some theoretical assumptions were verified.

2021

Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Authors
da Silva, DQ; Aguiar, AS; Dos Santos, FN; Sousa, AJ; Rabino, D; Biddoccu, M; Bagagiolo, G; Delmastro, M;

Publication
Agriculture

Abstract
Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

2021

A Camera to LiDAR calibration approach through the Optimization of Atomic Transformations

Authors
Pinto de Aguiar, AS; Riem de Oliveira, MA; Pedrosa, EF; Neves dos Santos, FB;

Publication
Expert Systems with Applications

Abstract

Supervised
thesis

2020

Grasping and manipulation with active perception for open-field agricultural robotics

Author
Sandro Augusto Costa Magalhães

Institution
UP-FEUP

2020

Localization and Mapping based on Semantic and Multi-Layer Maps Concepts

Author
André Silva Pinto de Aguiar

Institution
UP-FEUP

2020

Advanced 2.5D Path Planning for agricultural robots

Author
Luís Carlos Feliz Santos

Institution
UTAD

2018

Odometria visual em robôs para a agricultura com câmara(s) com lentes olho de peixe

Author
Sérgio Miguel Vieira Pinto

Institution
UP-FEUP

2018

Design and construction of cost effective VTOL drone for agricultural and forestry application

Author
Ahmad Safaee

Institution
UP-FEUP