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

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.

Automated summary

Cassava brown streak disease (CBSD) is an emerging viral disease that can greatly reduce cassava productivity. Current detection techniques are labor-intensive and inaccurate. We have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for real-time early detection of CBSD. The technique offers improved spectral signal-to-noise ratio and temporal repeatability, and can reliably distinguish healthy cassava from infected plants.