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About

About

José Boaventura-Cunha is an Engineer in Electronics and Telecommunications from the University of Aveiro (1985) and has a PhD in Electrotechnical and Computer Engineering from UTAD-University of Trás-os-Montes and Alto Douro, Portugal (2002). Currently holds the position of Associate Professor with habilitation at the the School of Sciences and Technology of UTAD.
Since 2012 he is a member of the CRIIS-Center for Robotics in Industry and Intelligent Systems at INESC TEC - Institute of Systems and Computer Engineering, Technology and Science and is Coordinator of the pole of INESC TEC at UTAD.
His research interests are related to the areas of Instrumentation, modeling and control applied to industrial and agro-forestry processes.

Interest
Topics
Details

Details

  • Name

    José Boaventura
  • Role

    Research Coordinator
  • Since

    01st June 2012
012
Publications

2023

2D LiDAR-Based System for Canopy Sensing in Smart Spraying Applications

Authors
Baltazar, AR; Dos Santos, FN; De Sousa, ML; Moreira, AP; Cunha, JB;

Publication
IEEE ACCESS

Abstract
The efficient application of phytochemical products in agriculture is a complex issue that demands optimised sprayers and variable rate technologies, which rely on advanced sensing systems to address challenges such as overdosage and product losses. This work developed a system capable of processing different tree canopy parameters to support precision fruit farming and environmental protection using intelligent spraying methodologies. This system is based on a 2D light detection and ranging (LiDAR) sensor and a Global Navigation Satellite System (GNSS) receiver integrated into a sprayer driven by a tractor. The algorithm detects the canopy boundaries, allowing spray only in the presence of vegetation. The spray volume spared evaluates the system's performance compared to a Tree Row Volume (TRV) methodology. The results showed a 28% reduction in the overdosage of spraying product. The second step in this work was calculating and adjusting the amount of liquid to apply based on the tree volume. Considering this parameter, the saving obtained had an average value for the right and left rows of 78%. The volume of the trees was also monitored in a georeferenced manner with the creation of a occupation grid map. This map recorded the trajectory of the sprayer and the detected trees according to their volume.

2023

Reagent-less spectroscopy towards NPK sensing for hydroponics nutrient solutions

Authors
Silva, FM; Queirós, C; Pinho, T; Boaventura, J; Santos, F; Barroso, TG; Pereira, MR; Cunha, M; Martins, RC;

Publication
SENSORS AND ACTUATORS B-CHEMICAL

Abstract
Nutrient quantification in hydroponic systems is essential. Reagent-less spectral quantification of nitrogen, phosphate and potassium faces challenges in accessing information-rich spectral signals and unscrambling interference from each constituent. Herein, we introduce information equivalence between spectra and sample composition, enabling extraction of consistent covariance to isolate nutrient-specific spectral information (N, P or K) in Hoagland nutrient solutions using orthogonal covariance modes. Chemometrics methods quantify nitrogen and potassium, but not phosphate. Orthogonal covariance modes, however, enable quantification of all three nutrients: nitrogen (N) with R = 0.9926 and standard error of 17.22 ppm, phosphate (P) with R = 0.9196 and standard error of 63.62 ppm, and potassium (K) with R = 0.9975 and standard error of 9.51 ppm. Including pH information significantly improves phosphate quantification (R = 0.9638, standard error: 43.16 ppm). Results demonstrate a direct relationship between spectra and Hoagland nutrient solution information, preserving NPK orthogonality and supporting orthogonal covariance modes. These modes enhance detection sensitivity by maximizing information of the constituent being quantified, while minimizing interferences from others. Orthogonal covariance modes predicted nitrogen (R = 0.9474, standard error: 29.95 ppm) accurately. Phosphate and potassium showed strong interference from contaminants, but most extrapolation samples were correctly diagnosed above the reference interval (83.26%). Despite potassium features outside the knowledge base, a significant correlation was obtained (R = 0.6751). Orthogonal covariance modes use unique N, P or K information for quantification, not spurious correlations due to fertilizer composition. This approach minimizes interferences during extrapolation to complex samples, a crucial step towards resilient nutrient management in hydroponics using spectroscopy.

2023

Application of Bio-Inspired Optimization Techniques for Wind Power Forecasting

Authors
Ferreira, J; Puga, R; Boaventura, J; Abtahi, A; Santos, S;

Publication
International Journal of Computer Information Systems and Industrial Management Applications

Abstract
As the need for replacing fossil and other non-renewable energy sources with renewables becomes more critical and urgent, wind energy appears to be among the two or three best choices for the short and medium time frames. The dominance of wind energy as the first choice in many regions, leads to an increasing impact of wind power quality on the overall grid. Wind energy’s inherent intermittent nature, both in intensity and longevity, could be an impediment to its adoption unless utility operators have the tools to anticipate the impact and integrate wind resources seamlessly by increasing or reducing its contribution to the overall capacity of the grid. The wind forecasting science is well established and has been the subject of serious study in multiple fields such as fluid dynamics, statistical analysis and numerical simulation and modeling. With the renewed interest and dependence on wind as a major energy source, these efforts have increased exponentially. One of the areas that shows great promise in developing improved forecasting tools, is the category of “Biological Inspired Optimization Techniques. The study presented in this paper is the result of a study to survey and assess an array of forecasting models and algorithms. © MIR Labs, www.mirlabs.net/ijcisim/index.html

2022

Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data

Authors
Aguiar, AS; dos Santos, FN; Sobreira, H; Boaventura Cunha, J; Sousa, AJ;

Publication
FRONTIERS IN ROBOTICS AND AI

Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.

2022

State of the Art of Wind and Power Prediction for Wind Farms

Authors
Puga, R; Baptista, J; Boaventura, J; Ferreira, J; Madureira, A;

Publication
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by Biological Inspired Optimization Techniques applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results.

Supervised
thesis

2022

Localization and Mapping Based on Semantic and Multi-layer Maps Concepts

Author
André Silva Pinto de Aguiar

Institution
UTAD

2022

Adaptive Grasping Planning: A Novel Unified and Modular Grasping Pipeline Architecture” (Planeamento de preensão adaptável: uma nova arquitetura de pipeline de agarramento unificado e modular)

Author
João Pedro Carvalho de Souza

Institution
UTAD

2021

Aerial high-resolution imagery to assess almond orchard conditions

Author
Nathalie dos Santos Guimarães

Institution
UTAD

2020

Identifying perishability cycles of fruits and vegetables with computer vision

Author
José Manuel Marques Vieira Rodrigues

Institution
UP-FEP

2020

Non-Intrusive Load Monitoring for Household Disaggregated Energy Sensing

Author
José Pedro Ferreira Pelicano Paulos

Institution
UP-FEP