A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

TL;DR

This paper reviews various image processing and machine learning techniques for leaf species identification, emphasizing feature extraction methods and classifiers to improve plant recognition systems.

Contribution

It provides a comprehensive overview of visual descriptors and classification methods used in leaf species identification, highlighting recent advancements and challenges.

Findings

Feature extraction is crucial for accurate leaf classification.

Various machine learning classifiers have been evaluated for species recognition.

Image shape, color, and texture are key features in leaf identification.

Abstract

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.