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Pest Localization Using YOLOv5 and Classification Based on Quantum Convolutional Network

Author

Listed:
  • Javeria Amin

    (Computer Science Department, University of Wah, Rawalpindi 47040, Pakistan)

  • Muhammad Almas Anjum

    (National University of Technology (NUTECH), Islamabad 44000, Pakistan)

  • Rida Zahra

    (Computer Science Department, University of Wah, Rawalpindi 47040, Pakistan)

  • Muhammad Imran Sharif

    (Department Computer Science, COMSATS University Islamabad, Wah Campus, Rawalpindi 47040, Pakistan)

  • Seifedine Kadry

    (Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon)

  • Lukas Sevcik

    (University of Zilina, 01026 Zilina, Slovakia)

Abstract

Pests are always the main source of field damage and severe crop output losses in agriculture. Currently, manually classifying and counting pests is time consuming, and enumeration of population accuracy might be affected by a variety of subjective measures. Additionally, due to pests’ various scales and behaviors, the current pest localization algorithms based on CNN are unsuitable for effective pest management in agriculture. To overcome the existing challenges, in this study, a method is developed for the localization and classification of pests. For localization purposes, the YOLOv5 is trained using the optimal learning hyperparameters which more accurately localize the pest region in plant images with 0.93 F1 scores. After localization, pest images are classified into Paddy with pest/Paddy without pest using the proposed quantum machine learning model, which consists of fifteen layers with two-qubit nodes. The proposed network is trained from scratch with optimal parameters that provide 99.9% classification accuracy. The achieved results are compared to the existing recent methods, which are performed on the same datasets to prove the novelty of the developed model.

Suggested Citation

  • Javeria Amin & Muhammad Almas Anjum & Rida Zahra & Muhammad Imran Sharif & Seifedine Kadry & Lukas Sevcik, 2023. "Pest Localization Using YOLOv5 and Classification Based on Quantum Convolutional Network," Agriculture, MDPI, vol. 13(3), pages 1-15, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:662-:d:1095466
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    References listed on IDEAS

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    1. Wei Zhang & Youqiang Sun & He Huang & Haotian Pei & Jiajia Sheng & Po Yang, 2022. "Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism," Agriculture, MDPI, vol. 12(8), pages 1-19, July.
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