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EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard

Author

Listed:
  • Dana Čirjak

    (Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, Croatia)

  • Ivan Aleksi

    (Department of Computer Engineering and Automation, Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia)

  • Darija Lemic

    (Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, Croatia)

  • Ivana Pajač Živković

    (Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, Croatia)

Abstract

Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:961-:d:1133509
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    References listed on IDEAS

    as
    1. 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.
    2. Jozsef Suto, 2022. "Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review," Agriculture, MDPI, vol. 12(10), pages 1-18, October.
    3. 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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    Cited by:

    1. Yuzhe Bai & Fengjun Hou & Xinyuan Fan & Weifan Lin & Jinghan Lu & Junyu Zhou & Dongchen Fan & Lin Li, 2023. "A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques," Agriculture, MDPI, vol. 13(9), pages 1-23, September.
    2. Jozsef Suto, 2023. "Hardware and Software Support for Insect Pest Management," Agriculture, MDPI, vol. 13(9), pages 1-2, September.

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