2022 journal article

Early detection of plant virus infection using multispectral imaging and spatial-spectral machine learning

SCIENTIFIC REPORTS, 12(1).

By: Y. Peng *, M. Dallas n, J. Ascencio-Ibanez  n, J. Hoyer *, J. Legg *, L. Hanley-Bowdoin n , B. Grieve *, H. Yin *

MeSH headings : Disease Resistance; Early Diagnosis; Machine Learning; Manihot / virology; Photometry / instrumentation; Photometry / methods; Plant Diseases / virology; Plant Viruses / genetics; Plant Viruses / pathogenicity; Potyviridae / pathogenicity; RNA, Viral; Spectrophotometry / instrumentation; Spectrophotometry / methods; Virus Diseases / diagnosis
TL;DR: A handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time is developed and has the potential to increase farmers’ access to healthy planting materials and reduce losses due toCBSD in Africa. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (Web of Science)
13. Climate Action (Web of Science)
Source: Web Of Science
Added: March 21, 2022

AbstractCassava brown streak disease (CBSD) is an emerging viral disease that can greatly reduce cassava productivity, while causing only mild aerial symptoms that develop late in infection. Early detection of CBSD enables better crop management and intervention. Current techniques require laboratory equipment and are labour intensive and often inaccurate. We have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time. The principal benefits of A-MSI over passive MSI and conventional camera systems are improved spectral signal-to-noise ratio and temporal repeatability. Information fusion techniques further combine spectral and spatial information to reliably identify features that distinguish healthy cassava from plants with CBSD as early as 28 days post inoculation on a susceptible and a tolerant cultivar. Application of the device has the potential to increase farmers’ access to healthy planting materials and reduce losses due to CBSD in Africa. It can also be adapted for sensing other biotic and abiotic stresses in real-world situations where plants are exposed to multiple pest, pathogen and environmental stresses.