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About

About

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 €).

Interest
Topics
Details

Details

  • Name

    Joaquim João Sousa
  • Role

    Senior Researcher
  • Since

    01st January 2014
004
Publications

2023

A deep learning approach for automatic counting of bedbugs and grape moth

Authors
Teixeira, AC; Morais, R; Sousa, J; Peres, E; Cunha, A;

Publication
Procedia Computer Science

Abstract

2023

A Systematic Review on Automatic Insect Detection Using Deep Learning

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

Publication
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

Using deep learning for automatic detection of insects in traps

Authors
Teixeira, AC; Morais, R; Sousa, J; Peres, E; Cunha, A;

Publication
Procedia Computer Science

Abstract

2023

Acacia dealbata classification from aerial imagery acquired using unmanned aerial vehicles

Authors
Pinto, J; Sousa, A; Sousa, J; Peres, E; Padua, L;

Publication
Procedia Computer Science

Abstract

2023

Exploratory approach for automatic detection of vine rows in terrace vineyards

Authors
Figueiredo, N; Padua, L; Cunha, A; Sousa, J; Sousa, A;

Publication
Procedia Computer Science

Abstract

Supervised
thesis

2021

Automatic analysis of UAS-based multi-temporal data as support to a precision agroforestry management

Author
Luís Filipe Machado Pádua

Institution
UTAD

2021

Artificial intelligence techniques applied to agriculture

Author
Nuno Leandro Soares de Figueiredo

Institution
UTAD

2021

Hyperspectral data analysis for agriculture applications

Author
Jonás Hruska

Institution
UTAD

2021

Irrigation management in olive groves with support of geomatics

Author
Pedro Miguel Mota Marques

Institution
UTAD

2021

Aerial high-resolution imagery to assess almond orchard conditions

Author
Nathalie dos Santos Guimarães

Institution
UTAD