An optical smartphone-based inspection platform for identification of diseased orchids

Kuan Chieh Lee, Yen Hsiang Wang, Wen Chun Wei, Ming Hsien Chiang, Ting En Dai, Chung Cheng Pan, Ting Yuan Chen, Shi Kai Luo, Po Kuan Li, Ju Kai Chen, Shien Kuei Liaw, Choa Feng Lin, Chin Cheng Wu, Jen Jie Chieh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Infections of orchids by the Odontoglossum ringspot virus or Cymbidium mosaic virus cause orchid disfiguration and are a substantial source of economic loss for orchid farms. Although immunoassays can identify these infections, immunoassays are expensive, time consuming, and labor consuming and limited to sampling-based testing methods. This study proposes a noncontact inspection platform that uses a spectrometer and Android smartphone. When orchid leaves are illuminated with a handheld optical probe, the Android app based on the Internet of Things and artificial intelligence can display the measured florescence spectrum and determine the infection status within 3 s by using an algorithm hosted on a remote server. The algorithm was trained on optical data and the results of polymerase chain reaction assays. The testing accuracy of the algorithm was 89%. The area under the receiver operating characteristic curve was 91%; thus, the platform with the algorithm was accurate and convenient for infection screening in orchids.

Original languageEnglish
Article number363
JournalBiosensors
Volume11
Issue number10
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • Artificial intelligence
  • Diseased orchids
  • Internet of Things
  • Optical inspection

ASJC Scopus subject areas

  • Clinical Biochemistry

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