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Rapid online plant leaf area change detection with high-throughput plant image data

Author

Listed:
  • Yinglun Zhan
  • Ruizhi Zhang
  • Yuzhen Zhou
  • Vincent Stoerger
  • Jeremy Hiller
  • Tala Awada
  • Yufeng Ge

Abstract

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

Suggested Citation

  • Yinglun Zhan & Ruizhi Zhang & Yuzhen Zhou & Vincent Stoerger & Jeremy Hiller & Tala Awada & Yufeng Ge, 2023. "Rapid online plant leaf area change detection with high-throughput plant image data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(14), pages 2984-2998, October.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2984-2998
    DOI: 10.1080/02664763.2022.2150753
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