  Plants are fundamentally important to life. Key research areas in plant
science include plant species identification, weed classification using hyper
spectral images, monitoring plant health and tracing leaf growth, and the
semantic interpretation of leaf information. Botanists easily identify plant
species by discriminating between the shape of the leaf, tip, base, leaf margin
and leaf vein, as well as the texture of the leaf and the arrangement of
leaflets of compound leaves. Because of the increasing demand for experts and
calls for biodiversity, there is a need for intelligent systems that recognize
and characterize leaves so as to scrutinize a particular species, the diseases
that affect them, the pattern of leaf growth, and so on. We review several
image processing methods in the feature extraction of leaves, given that
feature extraction is a crucial technique in computer vision. As computers
cannot comprehend images, they are required to be converted into features by
individually analysing image shapes, colours, textures and moments. Images that
look the same may deviate in terms of geometric and photometric variations. In
our study, we also discuss certain machine learning classifiers for an analysis
of different species of leaves.
