Early Detection of Plant Virus Infection Using Multispectral Imaging and Spatial-Spectral Machine Learning

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. 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.

Plant phenotyping relevance

携帯型マルチスペクトル撮像装置と機械学習を開発し、カンショのウイルス感染状態を植物画像から早期推定する方法が中心である。

Code and data availability

The paper's Data Availability section states the multispectral imaging dataset from the three Cassava-TME204-UCBSV trials is publicly deposited on Zenodo, matching the allowed URL exactly.