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Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review

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  • Jozsef Suto

    (Department of IT Systems and Networks, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary)

Abstract

The codling moth ( Cydia pomonella ) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. The conventional monitoring approach for insect swarming has been based on traps that are periodically checked by human operators. However, this workflow can be automatized. To achieve this goal, a dedicated image capture device and an accurate insect counter algorithm are necessary which make online insect swarm prediction possible. From the hardware side, more camera-equipped embedded systems have been designed to remotely capture and upload pest trap images. From the software side, with the aid of machine vision and machine learning methods, traditional (manual) identification and counting can be solved by algorithm. With the appropriate combination of the hardware and software components, spraying can be accurately scheduled, and the crop-defending cost will be significantly reduced. Although automatic traps have been developed for more pest species and there are a large number of papers which investigate insect detection, a limited number of articles focus on the C. pomonella . The aim of this paper is to review the state of the art of C. pomonella monitoring with camera-equipped traps. The paper presents the advantages and disadvantages of automated traps’ hardware and software components and examines their practical applicability.

Suggested Citation

  • Jozsef Suto, 2022. "Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review," Agriculture, MDPI, vol. 12(10), pages 1-18, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1721-:d:946847
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    References listed on IDEAS

    as
    1. Minxi Rong & Zhizheng Wang & Bin Ban & Xiaoli Guo & Fahad Al Basir, 2022. "Pest Identification and Counting of Yellow Plate in Field Based on Improved Mask R-CNN," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-9, March.
    2. Suk-Ju Hong & Sang-Yeon Kim & Eungchan Kim & Chang-Hyup Lee & Jung-Sup Lee & Dong-Soo Lee & Jiwoong Bang & Ghiseok Kim, 2020. "Moth Detection from Pheromone Trap Images Using Deep Learning Object Detectors," Agriculture, MDPI, vol. 10(5), pages 1-12, May.
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    Citations

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    Cited by:

    1. Jozsef Suto, 2023. "Hardware and Software Support for Insect Pest Management," Agriculture, MDPI, vol. 13(9), pages 1-2, September.
    2. Meixiang Chen & Liping Chen & Tongchuan Yi & Ruirui Zhang & Lang Xia & Cheng Qu & Gang Xu & Weijia Wang & Chenchen Ding & Qing Tang & Mingqi Wu, 2023. "Development of a Low-Power Automatic Monitoring System for Spodoptera frugiperda (J. E. Smith)," Agriculture, MDPI, vol. 13(4), pages 1-19, April.
    3. 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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