https://scholars.tari.gov.tw/handle/123456789/16389
Title: | An Optical Smartphone-Based Inspection Platform for Identification of Diseased Orchids | Authors: | 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 |
Keywords: | optical inspection;diseased orchids;artificial intelligence (ai);internet of things (iot) | Issue Date: | Oct-2021 | Publisher: | MDPI | Journal Volume: | 11 | Journal Issue: | 10 | Start page/Pages: | 363 | Source: | Biosensors-Basel | 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. |
URI: | https://www.mdpi.com/2079-6374/11/10/363 https://scholars.tari.gov.tw/handle/123456789/16389 |
ISSN: | 2079-6374 | DOI: | 10.3390/bios11100363 |
Appears in Collections: | SCI期刊 |
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