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

Yinglun Zhan, Ruizhi Zhang, Yuzhen Zhou, Vincent Stoerger, Jeremy Hiller, Tala Awada, Yufeng Ge

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2984-2998
Number of pages15
JournalJournal of Applied Statistics
Volume50
Issue number14
DOIs
StatePublished - 2023

Keywords

  • ADMM algorithm
  • Supervised learning
  • adaptive cusum
  • high-throughput plant phenotyping (HTPP)
  • online detection
  • plant leaf area

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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