Characterization of leaf surface phenotypes based on light interaction

Abstract

Background: Leaf surface phenotypes can indicate plant health and relate to a plant's adaptations to environmental stresses. Identifying these phenotypes using non-invasive techniques can assist in high-throughput phenotyping and can improve decision making in plant breeding. Identification of these surface phenotypes can also assist in stress identification. Incorporating surface phenotypes into leaf optical modelling can lead to improved biochemical parameter retrieval and species identification.

Results: In this paper, leaf surface phenotypes are characterized for 349 leaf samples based on polarized light reflectance measured at Brewster's Angle, and microscopic observation. Four main leaf surface phenotypes (glossy wax, glaucous wax, high trichome density, and glabrous) were identified for the leaf samples. The microscopic and visual observations of the phenotypes were used as ground truth for comparison with the spectral classification. In addition to surface classification, the microscope images were used to assess cell size, shape, and cell cap aspect ratios; these surface attributes were not found to correlate significantly with spectral measurements obtained in this study. Using a quadratic discriminant analysis function, a series of 10,000 classifications were run with the data randomly split between training and testing datasets, with 150 and 199 samples, respectively. The average correct classification rate was 72.9% with a worst-case classification of 60.3%.

Conclusions: Leaf surface phenotypes were successfully correlated with spectral measurements that can be obtained remotely. Remote identification of these surface phenotypes will improve leaf optical modelling and biochemical parameter estimations. Phenotyping of leaf surfaces can inform plant breeding decisions and assist with plant health monitoring.

Keywords: Leaves; Light; Polarization; Pubescence; Reflectance; Surface roughness; Wax.

Fig. 1

Leaf holder used to ensure the same part of the leaf is assessed in every stage of the data collection

Fig. 2

Microscope image of a black bean (Phaseolus vulgaris L.) leaf at 500 × magnification showing the user traced cell (A1) and computed convex hull (A2) used in cell size and margin undulation calculations

Fig. 3

3D reconstruction of a black bean (Phaseolus vulgaris L.) leaf showing possible points used for cell cap aspect ratio calculations. X and Y axis values are pixels (1 pixel = 0.098 µm) and Z axis values are in millimeters

Fig. 4

Reflectance factor with different polarizer orientations between parallel and perpendicular for a Virginia creeper (Parthenocissus quinquefolia (L.) Planch.) b Black bean (Phaseolus vulgaris L.) c Okra (Abelmoschus esculentus (L.) Moench) d Begonia (Begonia sp. L.) e Lemon (Citrus limon (L.) Osbeck) f Soy (Glycine max (L.) Merr.). These leaves correspond with the images in Fig. 5

Fig. 5

A variety of cell shapes and sizes at 500 × magnification for a Virginia creeper (Parthenocissus quinquefolia (L.) Planch.) b Black bean (Phaseolus vulgaris L.) c Okra (Abelmoschus esculentus (L.) Moench) d Begonia (Begonia sp. L.) e Lemon (Citrus limon (L.) Osbeck) f Soy (Glycine max (L.) Merr.)

Fig. 6

Comparison of leaf surface parameters to R_Qav value for a Cell Size b Margin Undulation c Cell Cap Aspect Ratio. Full dataset shown with black x, glabrous leaves shown with grey dot

Fig. 7

Images of leaves with a glossy, shiny wax a Anthurium (Anthurium sp. Schott) b Lemon (Citrus limon (L.) Osbeck) c Spiderwort (Tradescantia sp. L.). Microscope images at 500 × magnification are shown directly below each leaf sample

Fig. 8

Images of leaves with a glaucous wax a Jade (Crassula ovata (Miller) Druce (1917)) b Broccoli (Brassica oleracea L.) c Canola (Brassica napus L.). Microscope images at 500 × magnification are shown directly below each leaf sample

Fig. 9

Image of leaves with different types of non-glandular trichomes a Sunflower (Helianthus annuus L.) b Strawberry (Fragaria x ananassa Duchesne) c Oak (Quercus sp. L.). Microscope images at 100 × magnification are shown directly below each leaf sample

Fig. 10

The effect of pubescence on R_Qav value based on surface area percentage covered with trichomes

Fig. 11

Classification space of leaf surface phenotypes based on R_Qav and DIFF_R for one of 10,000 runs (classification rate was 74.9% in this example)

Fig. 12

Classification distribution results for 10,000 runs with random data splitting

Fig. 13

Classification distribution results for a glossy, b glaucous, c hairy, and d glabrous leaves