Detection of Apple Trees in Orchard Using Monocular Camera
Stephanie Nix (),
Airi Sato,
Hirokazu Madokoro,
Satoshi Yamamoto,
Yo Nishimura and
Kazuhito Sato
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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
Agriculture, 2025, vol. 15, issue 5, 1-14
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.
Keywords: smart farming; Single Shot MultiBox Detector; You Only Look Once; convolutional neural network; digital twin (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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