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Detection of Apple Trees in Orchard Using Monocular Camera

Author

Listed:
  • Stephanie Nix

    (Faculty of Software and Information Science, Iwate Prefectural University, Takizawa 020-0693, Japan)

  • Airi Sato

    (Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo 015-0055, Japan)

  • Hirokazu Madokoro

    (Faculty of Software and Information Science, Iwate Prefectural University, Takizawa 020-0693, Japan)

  • Satoshi Yamamoto

    (Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan)

  • Yo Nishimura

    (Agri-Innovation Education and Research Center, Akita Prefectural University, Ogata 010-0451, Japan)

  • Kazuhito Sato

    (Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo 015-0055, Japan)

Abstract

This study proposes an object detector for apple trees as a first step in developing agricultural digital twins. An original dataset of orchard images was created and used to train Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) models. Performance was evaluated using mean Average Precision (mAP). YOLO significantly outperformed SSD, achieving 91.3% mAP compared to the SSD’s 46.7%. Results indicate YOLO’s Darknet-53 backbone extracts more complex features suited to tree detection. This work demonstrates the potential of deep learning for automated data collection in smart farming applications.

Suggested Citation

  • Stephanie Nix & Airi Sato & Hirokazu Madokoro & Satoshi Yamamoto & Yo Nishimura & Kazuhito Sato, 2025. "Detection of Apple Trees in Orchard Using Monocular Camera," Agriculture, MDPI, vol. 15(5), pages 1-14, March.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:564-:d:1607002
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