· Input: - Features collected from half a million species of plant in the world.0
· Output: - Predicted species for leaves.
· Deep Learning Function: - Manipulating, analyzing, preprocessing the data, and training the data.
· Problems: - Classification of species has been historically problematic and often results in duplicate identifications.
· Objective: - The objective of this playground competition is to use binary leaf images and extracted features, including shape, margin & texture, to accurately identify 99 species of plants. Leaves, due to their volume, prevalence, and unique characteristics, are an effective means of differentiating plant species.
· The dataset consists of approximately 1,584 images of leaf specimens (16 samples each of 99 species) which have been converted to binary black leaves against white backgrounds. Three sets of features are also provided per image: a shape contiguous descriptor, an interior texture histogram, and a fine-scale margin histogram. For each feature, a 64-attribute vector is given per leaf sample and finally, it contains 193 Features.
· Note that of the original 100 species, we have eliminated one on account of incomplete associated data in the original dataset.