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Monitoring System for Leucoptera malifoliella (O. Costa, 1836) and Its Damage Based on Artificial Neural Networks

Author

Listed:
  • Dana Čirjak

    (Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 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, 31 000 Osijek, Croatia)

  • Ivana Miklečić

    (Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia)

  • Ana Marija Antolković

    (Department of Pomology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia)

  • Rea Vrtodušić

    (Department of Pomology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia)

  • Antonio Viduka

    (Department of Plant Nutrition, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia)

  • Darija Lemic

    (Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia)

  • Tomislav Kos

    (Department of Ecology, Agriculture and Aquaculture, University of Zadar, Trg Kneza Višeslava 9, 23 000 Zadar, Croatia)

  • Ivana Pajač Živković

    (Department of Agricultural Zoology, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10 000 Zagreb, Croatia)

Abstract

The pear leaf blister moth is a significant pest in apple orchards. It causes damage to apple leaves by forming circular mines. Its control depends on monitoring two events: the flight of the first generation and the development of mines up to 2 mm in size. Therefore, the aim of this study was to develop two models using artificial neural networks (ANNs) and two monitoring devices with cameras for the early detection of L . malifoliella (Pest Monitoring Device) and its mines on apple leaves (Vegetation Monitoring Device). To train the ANNs, 400 photos were collected and processed. There were 4700 annotations of L . malifoliella and 1880 annotations of mines. The results were processed using a confusion matrix. The accuracy of the model for the Pest Monitoring Device (camera in trap) was more than 98%, while the accuracy of the model for the Vegetation Monitoring Device (camera for damage) was more than 94%, all other parameters of the model were also satisfactory. The use of this comprehensive system allows reliable monitoring of pests and their damage in real-time, leading to targeted pest control, reduction in pesticide residues, and a lower ecological footprint. Furthermore, it could be adopted for monitoring other Lepidopteran pests in crop production.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:67-:d:1014825
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    References listed on IDEAS

    as
    1. Piotr Boniecki & Maciej Zaborowicz & Agnieszka Pilarska & Hanna Piekarska-Boniecka, 2020. "Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN," Agriculture, MDPI, vol. 10(6), pages 1-9, June.
    2. 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.
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