Detection of mosaic virus disease in cassava plants by sunlight-induced fluorescence imaging: a pilot study for proximal sensing

Cassava mosaic disease (CMD) is a prominent virus infection that causes considerable crop damage and yield reduction. Early detection of crop damage by remote sensing could be a useful tool for initiating remedial measures to reduce further crop damage. This article presents a non-destructive method for detection and classification of CMD infection, based on the red:far-red chlorophyll (chl) fluorescence image ratio. This pilot study was carried out in 14 varieties of potted cassava plants (Manihot esculenta Crantz) with a multispectral imaging system (MSIS) consisting of an electron multiplying charge coupled device (EMCCD) camera. Sunlight-induced chl fluorescence (SICF) images of plant leaves were recorded using the MSIS at the Fraunhofer lines of O₂-B at 687 nm and O₂-A at 759.5 nm and their off-lines at 684 and 757.5 nm. The recorded images were analysed using the Fraunhofer line discrimination (FLD) technique to extract the SICF from the solar reflectance in the recorded images. The chl fluorescence image ratio (red:far-red, F₆₈₇:F₇₆₀) was computed and correlated with the laser-induced chl fluorescence (LICF) ratio (F₆₈₅:F₇₃₅) determined by point monitoring, chl content variation, and the net photosynthetic rate (P_n). The scatter plot of the F₆₈₇:F₇₆₀ image ratio showed good discrimination between different levels of CMD infection as evidenced by the high sensitivity and specificity values. It is observed that the fluorescence image ratio (F₆₈₇:F₇₆₀) has a good correlation with P_n (coefficient of determination (R²) = 0.85), chl content (R² = 0.82), and the LICF ratio (F₆₈₅:F₇₃₅) (R² = 0.80), thereby highlighting the potential of the SICF image ratio in the discrimination of CMD infection. The results clearly indicate that changes in the red:far-red fluorescence image ratio due to CMD stress can easily be detected at an early stage and the technique has great potential for monitoring the health of crops and vegetation from proximal sensing platforms.