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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
013
Publications

2024

Precision Fertilization: A critical review analysis on sensing technologies for nitrogen, phosphorous and potassium quantification

Authors
Silva, F; Queirós, C; Pereira, M; Pinho, T; Barroso, T; Magalhães, S; Boaventura, J; Santos, F; Cunha, M; Martins, R;

Publication
Computers and Electronics in Agriculture

Abstract

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

2023

Sound-Based Anomalies Detection in Agricultural Robotics Application

Authors
Baltazar, AR; dos Santos, FN; Soares, SP; Moreira, AP; Cunha, JB;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT II

Abstract
Agricultural robots are exposed to adverse conditions reducing the components' lifetime. To reduce the number of inspection, repair and maintenance activities, we propose using audio-based systems to diagnose and detect anomalies in these robots. Audio-based systems are non-destructive/intrusive solutions. Besides, it provides a significant amount of data to diagnose problems and for a wiser scheduler for preventive activities. So, in this work, we installed two microphones in an agricultural robot with a mowing tool. Real audio data was collected with the robotic mowing tool operating in several conditions and stages. Besides, a Sound-based Anomalies Detector (SAD) is proposed and tested with this dataset. The SAD considers a short-time Fourier transform (STFT) computation stage connected to a Support Vector Machine (SVM) classifier. The results with the collected dataset showed an F1 score between 95% and 100% in detecting anomalies in a mowing robot operation.

Supervised
thesis

2023

Planejamentode preensão adaptável: uma nova arquitetura de Pipeline de agarramento unificado e modular

Author
João Pedro Carvalho de Souza

Institution
UTAD

2023

Adaptive Grasping Planning: A Novel Unified and Modular Grasping Pipeline Architecture

Author
João Pedro Carvalho de Souza

Institution
UTAD

2023

Sistema autónomo para reaproveitamento de águas quentes do banho

Author
Luís Miguel Sampaio Sanches Ferreira

Institution
UTAD

2023

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

Author
André Silva Pinto de Aguiar

Institution
UTAD

2022

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

Author
André Silva Pinto de Aguiar

Institution
UTAD