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Automatic Detection and Monitoring of Insect Pests—A Review

Author

Listed:
  • Matheus Cardim Ferreira Lima

    (Department of Agroforest Ecosystems, Polytechnic University of Valencia, 46022 Valencia, Spain)

  • Maria Elisa Damascena de Almeida Leandro

    (Department of Crop Protection, Faculty of Agricultural Sciences, University of Göttingen, 37077 Göttingen, Germany)

  • Constantino Valero

    (Department of Agroforest Engineering, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain)

  • Luis Carlos Pereira Coronel

    (Department of Civil, Industrial and Environmental Engineering (DICIA), Faculty of Science and Technology, Catholic University of Asunción (UCA), Asunción, Paraguay)

  • Clara Oliva Gonçalves Bazzo

    (Agriculture Department, City Hall of Parauapebas, Parauapebas 66515000, Brazil)

Abstract

Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

Suggested Citation

  • Matheus Cardim Ferreira Lima & Maria Elisa Damascena de Almeida Leandro & Constantino Valero & Luis Carlos Pereira Coronel & Clara Oliva Gonçalves Bazzo, 2020. "Automatic Detection and Monitoring of Insect Pests—A Review," Agriculture, MDPI, vol. 10(5), pages 1-24, May.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:5:p:161-:d:356023
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    References listed on IDEAS

    as
    1. Corey J. A. Bradshaw & Boris Leroy & Céline Bellard & David Roiz & Céline Albert & Alice Fournier & Morgane Barbet-Massin & Jean-Michel Salles & Frédéric Simard & Franck Courchamp, 2016. "Massive yet grossly underestimated global costs of invasive insects," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
    2. Okuyama, Toshinori & Yang, En-Cheng & Chen, Chia-Pang & Lin, Tzu-Shiang & Chuang, Cheng-Long & Jiang, Joe-Air, 2011. "Using automated monitoring systems to uncover pest population dynamics in agricultural fields," Agricultural Systems, Elsevier, vol. 104(9), pages 666-670.
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    Cited by:

    1. Wei Li & Tengfei Zhu & Xiaoyu Li & Jianzhang Dong & Jun Liu, 2022. "Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection," Agriculture, MDPI, vol. 12(7), pages 1-17, July.
    2. Dana Čirjak & Ivan Aleksi & Ivana Miklečić & Ana Marija Antolković & Rea Vrtodušić & Antonio Viduka & Darija Lemic & Tomislav Kos & Ivana Pajač Živković, 2022. "Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    3. Ana Cláudia Teixeira & José Ribeiro & Raul Morais & Joaquim J. Sousa & António Cunha, 2023. "A Systematic Review on Automatic Insect Detection Using Deep Learning," Agriculture, MDPI, vol. 13(3), pages 1-24, March.
    4. Jorge Mendes & Emanuel Peres & Filipe Neves dos Santos & Nuno Silva & Renato Silva & Joaquim João Sousa & Isabel Cortez & Raul Morais, 2022. "VineInspector: The Vineyard Assistant," Agriculture, MDPI, vol. 12(5), pages 1-23, May.
    5. Mikhail A. Genaev & Evgenii G. Komyshev & Olga D. Shishkina & Natalya V. Adonyeva & Evgenia K. Karpova & Nataly E. Gruntenko & Lyudmila P. Zakharenko & Vasily S. Koval & Dmitry A. Afonnikov, 2022. "Classification of Fruit Flies by Gender in Images Using Smartphones and the YOLOv4-Tiny Neural Network," Mathematics, MDPI, vol. 10(3), pages 1-19, January.

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