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.
Highlights
Orchid cultivation is a crucial industry globally because of the wide use of ornamental plants for festivals or commercial purposes
Other parameters and methods using commercial statistical software have been investigated in the literature [15]. These algorithmic methods remain limited to well-controlled experiments with significant signals; artificial intelligence (AI)-based methods have been validated for complex applications [16,17,18]
The dilute chlorophyll extract solutions from the Cymbidium mosaic virus (CyMV) or Odontoglossum ringspot virus (ORSV) plants were applied to their pot soil and leaves to incubate the disease
Summary
Orchid cultivation is a crucial industry globally because of the wide use of ornamental plants for festivals or commercial purposes. Used test-strip immunoassays [2,3] and enzyme-linked immunosorbent assays [4] have poor performance due to their brief effective timeframes, in addition to susceptibility to operator error and limited sensitivity. Polymerase chain reaction (PCR)—has the highest sensitivity but is the costliest [8] Both conventional and newer biological assays are suitable for only sampling inspection but not for general screening. Other parameters and methods using commercial statistical software have been investigated in the literature [15] These algorithmic methods remain limited to well-controlled experiments with significant signals; artificial intelligence (AI)-based methods have been validated for complex applications [16,17,18]. A cloud-based database and AI algorithm were developed, and an Android app was coded to display the results of the measurement and algorithm
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