ArticleScientific reports2022
Early detection of plant virus infection using multispectral imaging and spatial-spectral machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Emerging technologies for in-field plant virus detection: innovations and future directions.The Journal of general virology · 2025Review
- Review
- Prediction of soybean yellow mottle mosaic virus in soybean using hyperspectral imaging.Plant methods · 2025Article
- 3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging.Plant methods · 2025Article
- Article
- A hyperspectral imaging and machine learning approach for rapid and non-invasive diagnosis of cassava bacterial blight.Frontiers in plant science · 2025Article
- Machine learning-enabled UAV hyperspectral identification of tomato spotted wilt virus in tobacco.Frontiers in plant science · 2025Article
- Exploring Imaging Techniques for Detecting Tomato Spotted Wilt Virus (TSWV) Infection in Pepper (Plants (Basel, Switzerland) · 2024Article
- Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach.Plant methods · 2024Article
- Direct and indirect technical guide for the early detection and management of fungal plant diseases.Current research in microbial sciences · 2024Review
- Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning.Scientific reports · 2023Article
- Assembly of plant virus agroinfectious clones using biological material or DNA synthesis.STAR protocols · 2022Article
- Virus Diseases of Cereal and Oilseed Crops in Australia: Current Position and Future Challenges.Viruses · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cassava 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.
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