Leaf identification data (Flavia)

Description

Image analysis of leaves to predict species.

Details

From the original manuscript: "The Flavia dataset contains 1907 leaf images. There are 32 different species and each has 50-77 images. Scanners and digital cameras are used to acquire the leaf images on a plain background. The isolated leaf images contain blades only, without a petiole. These leaf images are collected from the most common plants in Yangtze, Delta, China. Those leaves were sampled on the campus of the Nanjing University and the Sun Yat-Sen arboretum, Nanking, China."

The reference below has details information on the features used for prediction.

Columns:

• species: factor (32 levels)

• apex: factor (9 levels)

• base: factor (6 levels)

• shape: factor (5 levels)

• denate_edge: factor (levels: 'no' and 'yes')

• lobed_edge: factor (levels: 'no' and 'yes')

• smooth_edge: factor (levels: 'no' and 'yes')

• toothed_edge: factor (levels: 'no' and 'yes')

• undulate_edge: factor (levels: 'no' and 'yes')

• outlying_polar: numeric

• skewed_polar: numeric

• clumpy_polar: numeric

• sparse_polar: numeric

• striated_polar: numeric

• convex_polar: numeric

• skinny_polar: numeric

• stringy_polar: numeric

• monotonic_polar: numeric

• outlying_contour: numeric

• skewed_contour: numeric

• clumpy_contour: numeric

• sparse_contour: numeric

• striated_contour: numeric

• convex_contour: numeric

• skinny_contour: numeric

• stringy_contour: numeric

• monotonic_contour: numeric

• num_max_ponits: numeric

• num_min_points: numeric

• diameter: numeric

• area: numeric

• perimeter: numeric

• physiological_length: numeric

• physiological_width: numeric

• aspect_ratio: numeric

• rectangularity: numeric

• circularity: numeric

• compactness: numeric

• narrow_factor: numeric

• perimeter_ratio_diameter: numeric

• perimeter_ratio_length: numeric

• perimeter_ratio_lw: numeric

• num_convex_points: numeric

• perimeter_convexity: numeric

• area_convexity: numeric

• area_ratio_convexity: numeric

• equivalent_diameter: numeric

• eccentriciry: numeric

• contrast: numeric

• correlation_texture: numeric

• inverse_difference_moments: numeric

• entropy: numeric

• mean_red_val: numeric

• mean_green_val: numeric

• mean_blue_val: numeric

• std_red_val: numeric

• std_green_val: numeric

• std_blue_val: numeric

• correlation: numeric

Value

Source

Lakshika, Jayani PG, and Thiyanga S. Talagala. "Computer-aided interpretable features for leaf image classification." arXiv preprint arXiv:2106.08077 (2021).

https://github.com/SMART-Research/leaffeatures_paper

Examples

data(leaf_id_flavia)
str(leaf_id_flavia)