Applying extreme learning machine to plant species identification

Zhai, Chuan-Min; Du, Ji-Xiang

doi:10.1109/icinfa.2008.4608123

Cited by 9 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, according to the idea of centered inputs in [18,19], HFSR in Eq. (8) can be equivalently realized by carrying out the corresponding HFSR for centered inputs.…”

Section: Centered-elmmentioning

confidence: 99%

“…In order to overcome these shortcomings of these learning algorithms, Huang et al proposed the extreme learning machine (ELM) for single hidden layer feedforward neural networks (SLFN) [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. They proved that the input weights and the hidden layer biases can be randomly assigned if the activation function in the hidden layer is infinitely differentiable.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fast learning method for feedforward neural networks

et al. 2015

View full text Add to dashboard Cite

“…In fact, according to the idea of centered inputs in [18,19], HFSR in Eq. (8) can be equivalently realized by carrying out the corresponding HFSR for centered inputs.…”

Section: Centered-elmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fast learning method for feedforward neural networks

et al. 2015

View full text Add to dashboard Cite

“…There has been a study of several methods for recognizing plants by studying their leaf texture, shape, color and venation. Some of the methods include Gabor Filters (GF), Fractal Dimensions (FracDim), Gray Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradient (HoG) for leaf texture; Casanova et al, 2009;Cope et al, 2010;Kebapci et al, 2011;Rossatto et al, 2011;Sá et al, 2013;Syahputra et al, 2014;Zhai and Du, 2008), Simple and Morphological Shape Descriptors (SMSD), Hu moments, Fourier Descriptor (FD), Tchebichef Moment Invariant (TMI), Centroid Contour Distance (CCD), Zernike Moment Invariant (ZMI), Harmonic mean projecting transform for leaf shape; (Aakif and Khan, 2015;Chaki et al, 2015b;Du et al, 2007;Hossain and Amin, 2010;Kadir et al, 2011b;Lee and Chen, 2006;Teng et al, 2009;Zahra et al, 2020), Color Moments (CM), Color Histograms (CH), Color Co-occurrence Matrices (CCM) for leaf color; (Caglayan et al, 2013;Che Hussin et al, 2013;Kebapci et al, 2011;Prasad et al, 2013;Yanikoglu et al, 2014). Areoles morphology, Leaf vein and Run-length features for leaf venation; (Gu et al, 2005;Larese et al, 2012;Larese et al, 2014a;2014b)…”

Section: Plant Identificationmentioning

confidence: 99%

Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review

Oppong¹,

Twum²,

Hayfron-Acquah³

et al. 2021

Journal of Computer Science

View full text Add to dashboard Cite

Computer-aided identification of plants is a branch of machine learning that has become more recognized recently and proves itself as a vital tool in numerous sectors including pharmacological science, forestry and agriculture. This has essentially generated a zeal in creating automated systems for the identification of diverse species of plants. This study reviewed plant species classification relying on leaf textural features using Gabor filters and revealed that Gabor filters perform better when combined with other feature extraction methods. Therefore, this study proposes using Log-Gabor filter in the field of plant identification to improve accuracy since they overcome the drawbacks of Gabor filters which are; the maximum bandwidth of a Gabor filter is limited to approximately one octave and Gabor filters are not optimal if one is seeking broad spectral information with maximal spatial localization.

show abstract

“…The researchers in the paper [89] used BPN to train herbs of 600 training samples and 1400 test samples with the texture feature and achieved an average accuracy of 98.9%. The authors of paper [131] accounted the single hidden layer feed forward network for classification. There is no need to use a kernel function to approximate the weights, given that it updates the weights randomly for fixed bias inputs.…”

Section: Fig 4 General Classification Techniquementioning

confidence: 99%

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

Thyagharajan¹,

Raji²

2018

Arch Computat Methods Eng

View full text Add to dashboard Cite

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. Table 2 Inferences of data sources Visual descriptors of leaf and classification No. of papers selected Geometrical descriptors 30 Leaf shape/tip/base/venation 40 Texture/texton 25

show abstract

Applying extreme learning machine to plant species identification

Cited by 9 publications

References 9 publications

A fast learning method for feedforward neural networks

A fast learning method for feedforward neural networks

Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review

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

Contact Info

Product

Resources

About