In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging

The objective of this study was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation and distribution in leaves using hyperspectral imaging. The hyperspectral imaging data cube of cucumber (Cucumis sativus) leaves in the range of 450–850 nm was investigated and preprocessed. Sixty optical signatures or indices as a function of the associated reflectance (Rλ) at the special wavelength (λ) nm which proposed in the literatures were used to predict the total chlorophyll content in cucumber leaves. Finally, R710/R760, (R780 − R710)/(R780 − R680), (R750 − R705)/(R750 + R705), (R680 − R430)/(R680 + R430), R860/(R550 × R708), (R695−705)−1 − (R750−800)−1, and REP-LEM (a index based on red edge position and estimated with a linear extrapolation method) were identified as optimum indices. Red-edge waveband (680–780 nm) appeared in all these optimum indices, indicating the importance of REP (red edge position) in chlorophyll estimation. When (R695−705)−1 − (R750−800)−1, the best index was applied to an independent validation set, chlorophyll content (r = 0.8286) were reasonably well predicted, indicating model robustness. Depending on the sample, this technique enables to identify and characterize the relative content of various chlorophyll that distribution in the cucumber leaves. The map shows a relatively low level of chlorophyll at margins. Higher level can be noticed in the regions along the main veins and in some areas exhibiting dark green tissue. Our results indicate that hyperspectral imaging has considerable promise for predicting pigments in leaves and, the pigments can be detected in situ in living plant samples non-destructively.

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► Hyperspectral imaging for noninvasive chlorophyll estimation and distribution in cucumber leaves.
► Simple sixty optical signature or indices for chlorophyll estimation.
► Optimum index showed its robustness for an independent validation set.
► Spatial localization of chlorophyll accumulation could be mapped in vivo.