Comparative study of leaf image recognition with a novel learning-based approach

Abstract

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse codingbased framework can perform better.

What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora

“… Wäldchen and Mäder 2018 ). Many of the earlier studies of automated identification focussed on images of single organs, especially leaves, imaged on plain backgrounds ( Wang et al 2003 ; Agarwal et al 2006 ; Valliammal and Geethalakshmi 2011 ; Hati and Sajeevan 2013 ; Satti et al 2013 ; Shayan and Sajeevan 2013 ; Hsaio et al 2014 ; Zhao et al 2015 ; Lee et al 2017 ; Wäldchen and Mäder 2018 ). Much effort has been centred on the Cross Language Evaluation Forum (CLEF) initiative ( http://www.clef-initiative.eu/association ), which since 2013 included the LifeCLEF challenge to develop automated identification systems.…”

Some New Types of Approximations via Minimal Structure

“…The classification process is based on the classifiers of machine learning such as Support Vector Machine (SVM), decision tree (DT), Naïve Bayes (NB), Naïve Bayes Tree (NBT). A comparative analysis of the leaf image recognition has been presented in [22] by using SVM classifier with 95.47 % accuracy results. An expert system of plant identification has been proposed in [23] to identify different species of plant based on their images of leaves.…”

Leaf Image Recognition Based on Bag of Features

“…Hence, it is reasonable to use both shape feature and texture feature for leaf recognition. The most commonly used texture features contain entropy sequence (EnS) [10], histogram of gradients (HOG) [11], Zernike moments [12], scale invariant feature transform (SIFT) [13,14], gray-level co-occurrence matrix (GLCM) [15], and local binary patterns (LBP) [15]. Fu et al [16] proposed a hybrid framework for plant recognition with complicated background.…”