Prediction of Leaves Using Convolutional Neural Network

Abhishek Agarwal; Rohini Venkat

doi:doi:10.11648/j.ijiis.20200904.12

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Prediction of Leaves Using Convolutional Neural Network

Abhishek Agarwal, Rohini Venkat

Published in International Journal of Intelligent Information Systems (Volume 9, Issue 4)

Received: 24 February 2020 Accepted: 17 March 2020 Published: 27 October 2020

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Abstract

Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.

Published in	International Journal of Intelligent Information Systems (Volume 9, Issue 4)
DOI	10.11648/j.ijiis.20200904.12
Page(s)	35-38
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

CNN, OpenCV, Google Collab, Leaf Classification

References

[1]	Mohammad Aminul Islam, Md. Sayeed Iftekhar Yousuf and M. M. Billah, “Automatic Plant Detection Using HOG and LBP Features With SVM”, International Journal of Computer (IJC), 2019.
[2]	Jyotismita Chaki and Ranjan Parekh, “Plant Leaf Recognition using Shape based Features and Neural Network classifiers”, International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 2, No. 10, 2011.
[3]	Naveen Kumar Singla, “PLANT LEAF IMAGE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK, “Plant Leaf Image Classification Using Artificial Neural Network/”, A Dissertation submitted in fulfillment of the requirements for the Degree of Master Of Engineering in Electronic Instrumentation & Control Engineering, 2015.
[4]	ViNguyen Thanh Le, Beniamin Apopei and Kamal Alameh, “Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods”, Information Processing in Agriculture, Volume 6, Issue 1, March 2019.
[5]	Milan Sulc and Jiri Matas, “Fine-grained recognition of plants from images”, Plant Methods, 2017.
[6]	Vijai Singh and A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques, The Journal of the China Agricultural University, 2017.
[7]	Sujatha R, Y Sravan Kumar and Garine Uma Akhil, “Leaf disease detection using image processing”, Journal of Chemical and Pharmaceutical Sciences JCPS Volume 10 Issue 1, January - March 2017.
[8]	Zhi-Hua Xie, Jie Zeng, Guo-Dong Liu and Zhi-Jun Fang, “A novel infrared face recognition based on local binary pattern”, International Conference on Wavelet Analysis and Pattern Recognition, 2011.
[9]	Anna Liza A. Ramos, Dania May P. Aguila Anne, Catlyne B. Karunungan, Jon-Jon B. Patiño, Vincent L. Polintan, “Face Recognition With Or Without Makeup Using Haar Cascade Classifier Algorithm And Local Binary Pattern Histogram Algorithm”, International Research Journal of Computer Science (IRJCS), Issue 04, Volume 6 (April 2019).
[10]	Abdulrahman Alreshidi, Hina Afridi and Wilayat Khan, International Journal of Research in Science and Technology (IJRST), 2019.
[11]	Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, Yizhou Yu, “HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition“, IEEE International Conference on Computer Vision, 2015.
[12]	https://github.com/spMohanty/PlantVillage-Dataset.

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APA Style

Abhishek Agarwal, Rohini Venkat. (2020). Prediction of Leaves Using Convolutional Neural Network. International Journal of Intelligent Information Systems, 9(4), 35-38. https://doi.org/10.11648/j.ijiis.20200904.12

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ACS Style

Abhishek Agarwal; Rohini Venkat. Prediction of Leaves Using Convolutional Neural Network. Int. J. Intell. Inf. Syst. 2020, 9(4), 35-38. doi: 10.11648/j.ijiis.20200904.12

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AMA Style

Abhishek Agarwal, Rohini Venkat. Prediction of Leaves Using Convolutional Neural Network. Int J Intell Inf Syst. 2020;9(4):35-38. doi: 10.11648/j.ijiis.20200904.12

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@article{10.11648/j.ijiis.20200904.12,
  author = {Abhishek Agarwal and Rohini Venkat},
  title = {Prediction of Leaves Using Convolutional Neural Network},
  journal = {International Journal of Intelligent Information Systems},
  volume = {9},
  number = {4},
  pages = {35-38},
  doi = {10.11648/j.ijiis.20200904.12},
  url = {https://doi.org/10.11648/j.ijiis.20200904.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20200904.12},
  abstract = {Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.},
 year = {2020}
}

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TY  - JOUR
T1  - Prediction of Leaves Using Convolutional Neural Network
AU  - Abhishek Agarwal
AU  - Rohini Venkat
Y1  - 2020/10/27
PY  - 2020
N1  - https://doi.org/10.11648/j.ijiis.20200904.12
DO  - 10.11648/j.ijiis.20200904.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 35
EP  - 38
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20200904.12
AB  - Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.
VL  - 9
IS  - 4
ER  -

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Author Information

Abhishek Agarwal

Computer Science Department, Christ University, Bangalore, India
Rohini Venkat

Computer Science Department, Christ University, Bangalore, India

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Plain Text BibTeX RIS

APA Style

Abhishek Agarwal, Rohini Venkat. (2020). Prediction of Leaves Using Convolutional Neural Network. International Journal of Intelligent Information Systems, 9(4), 35-38. https://doi.org/10.11648/j.ijiis.20200904.12

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ACS Style

Abhishek Agarwal; Rohini Venkat. Prediction of Leaves Using Convolutional Neural Network. Int. J. Intell. Inf. Syst. 2020, 9(4), 35-38. doi: 10.11648/j.ijiis.20200904.12

Copy | Download

AMA Style

Abhishek Agarwal, Rohini Venkat. Prediction of Leaves Using Convolutional Neural Network. Int J Intell Inf Syst. 2020;9(4):35-38. doi: 10.11648/j.ijiis.20200904.12

Copy | Download

@article{10.11648/j.ijiis.20200904.12,
  author = {Abhishek Agarwal and Rohini Venkat},
  title = {Prediction of Leaves Using Convolutional Neural Network},
  journal = {International Journal of Intelligent Information Systems},
  volume = {9},
  number = {4},
  pages = {35-38},
  doi = {10.11648/j.ijiis.20200904.12},
  url = {https://doi.org/10.11648/j.ijiis.20200904.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20200904.12},
  abstract = {Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Prediction of Leaves Using Convolutional Neural Network
AU  - Abhishek Agarwal
AU  - Rohini Venkat
Y1  - 2020/10/27
PY  - 2020
N1  - https://doi.org/10.11648/j.ijiis.20200904.12
DO  - 10.11648/j.ijiis.20200904.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 35
EP  - 38
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20200904.12
AB  - Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.
VL  - 9
IS  - 4
ER  -

Copy | Download