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Sobre

Sobre

Professor Auxiliar com Agregação da Universidade de Trás-os-Montes e Alto Douro (UTAD) e doutorado em Ciências da Engenharia Geográfica, pela Universidade do Porto e pela Universidade de Delft (Holanda), tendo apresenta a tese “Potential of integrating PSInSAR Methodologies in the Detection of Surface Deformation”. Atualmente, é Investigador (membro integrado) do Centre for Robotics in Industry and Intelligent Systems (CRISS), do INESC TEC/Polo UTAD, e investigador (colaborador) do CITAB (Centre for the Research and Technology of Agro-Environmental and Biological Sciences). Nos últimos anos tem-se dedicado, sobretudo, à utilização de Veículos Aéreos Não Tripulados (UAV) para aplicações agroflorestais. Utiliza imagens aéreas de elevada resolução, obtidas por diferentes sensores (RGB, NIR, Multiespectrais, Hiperespectrais e Térmicos) para, usando técnicas de processamento de imagem e desenvolvimento de algoritmos, extrair informações e parâmetros relevantes, sobretudo, na vinha, soutos e olivais. Estas técnicas são, no entanto, extensíveis à deteção e monitorização de grande parte das espécies arbóreas, que integram as nossas florestas, e de vegetação rasteira. É autor de várias publicações em revistas internacionais da especialidade do Remote Sensing. Participa em vários projetos de investigação, destacando-se o PARRA (Plataforma integrAda de monitoRização e avaliação da doença da flavescência douRada na vinha), em que é líder por parte da UTAD (SI I&DT, aviso Nº 08/SI/2015, Projeto em Co-Promoção, parceiros do projeto: TEKEVER ASDS - empresa líder, UTAD, Instituto Politécnico de Viana do Castelo, INIAV, Agrociência. Montante total atribuído 1.602.245,58€) e é membro do projeto Plataforma de Inovação da Vinha e do Vinho, linha Remote sensing and detection of grapevine diseases (Projeto I&DT pelo Norte2020, com um financiamento global de 4.500.000,00 €).

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Joaquim João Sousa
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2014
  • Nacionalidade

    Portugal
  • Contactos

    +351220413317
    joaquim.j.sousa@inesctec.pt
004
Publicações

2024

Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction

Autores
Guimarães N.; Fraga H.; Sousa J.J.; Pádua L.; Bento A.; Couto P.;

Publicação
AgriEngineering

Abstract
Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Trás-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.

2023

A Systematic Review on Automatic Insect Detection Using Deep Learning

Autores
Teixeira, AC; Ribeiro, J; Morais, R; Sousa, JJ; Cunha, A;

Publicação
AGRICULTURE-BASEL

Abstract
Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an alternative, integrated pest management has been devised to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. With the improvements in artificial intelligence technologies, several applications have emerged in the agricultural context, including automatic detection, monitoring, and identification of insects. The purpose of this article is to outline the leading techniques for the automated detection of insects, highlighting the most successful approaches and methodologies while also drawing attention to the remaining challenges and gaps in this area. The aim is to furnish the reader with an overview of the major developments in this field. This study analysed 92 studies published between 2016 and 2022 on the automatic detection of insects in traps using deep learning techniques. The search was conducted on six electronic databases, and 36 articles met the inclusion criteria. The inclusion criteria were studies that applied deep learning techniques for insect classification, counting, and detection, written in English. The selection process involved analysing the title, keywords, and abstract of each study, resulting in the exclusion of 33 articles. The remaining 36 articles included 12 for the classification task and 24 for the detection task. Two main approaches-standard and adaptable-for insect detection were identified, with various architectures and detectors. The accuracy of the classification was found to be most influenced by dataset size, while detection was significantly affected by the number of classes and dataset size. The study also highlights two challenges and recommendations, namely, dataset characteristics (such as unbalanced classes and incomplete annotation) and methodologies (such as the limitations of algorithms for small objects and the lack of information about small insects). To overcome these challenges, further research is recommended to improve insect pest management practices. This research should focus on addressing the limitations and challenges identified in this article to ensure more effective insect pest management.

2023

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

Autores
Teixeira, I; Morais, R; Sousa, JJ; Cunha, A;

Publicação
AGRICULTURE-BASEL

Abstract
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

2023

Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral data

Autores
Guimaraes, N; Padua, L; Sousa, JJ; Bento, A; Couto, P;

Publicação
INTERNATIONAL JOURNAL OF REMOTE SENSING

Abstract
In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.

2023

MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the Benínar Dam (Almería, Spain)

Autores
Marchamalo-Sacristán, M; Ruiz-Armenteros, AM; Lamas-Fernández, F; González-Rodrigo, B; Martínez-Marín, R; Delgado-Blasco, JM; Bakon, M; Lazecky, M; Perissin, D; Papco, J; Sousa, JJ;

Publicação
Remote Sensing

Abstract
The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of -6 mm/year (1992–2000), -4 mm/year (2002–2010), and -2 mm/year (2015–2021) with median values of -2.6 and -3.0 mm/year in the first periods (ERS and Envisat) and -1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring.

Teses
supervisionadas

2023

Phase unwrapping using ml methods

Autor
Diogo Gabriel da Silva Couto

Instituição
UTAD

2022

Finding patterns that predict hyper and hypoglycaemia

Autor
Ricardo Bruno Ferreira de Faria

Instituição
UP-FCUP

2021

Irrigation management in olive groves with support of geomatics

Autor
Pedro Miguel Mota Marques

Instituição
UTAD

2021

Aerial high-resolution imagery to assess almond orchard conditions

Autor
Nathalie dos Santos Guimarães

Instituição
UTAD

2021

Automatic detection of earthquake's deformations in SAR interferograms

Autor
Bruno Miguel Ferreira Silva

Instituição