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A Systematic Review on Automatic Insect Detection Using Deep Learning

Author

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  • Ana Cláudia Teixeira

    (Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal)

  • José Ribeiro

    (Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal)

  • Raul Morais

    (Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal)

  • Joaquim J. Sousa

    (Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal)

  • António Cunha

    (Engineering Department, School of Science and Technology, UTAD—University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal)

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.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:713-:d:1101441
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    References listed on IDEAS

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    1. 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.
    2. Nigel E. Stork, 2007. "World of insects," Nature, Nature, vol. 448(7154), pages 657-658, August.
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    Cited by:

    1. Dana Čirjak & Ivan Aleksi & Darija Lemic & Ivana Pajač Živković, 2023. "EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard," Agriculture, MDPI, vol. 13(5), pages 1-20, April.

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