Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms

Joel Bejar Mallma; Ciro Rodriguez; Yuri Pomachagua; Carlos Navarro

doi:10.1109/CICN51697.2021.9574669

Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms

Joel Bejar Mallma, Ciro Rodriguez, Yuri Pomachagua, Carlos Navarro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Plant leaf diseases usually affect agriculture a lot, which is one of the important sources of income for people, so diseases must be detected and recognized quickly and effectively. The research aims to identify these diseases automatically using a model based on deep learning known as convolutional neural networks and the K-means algorithm. The methodology applied for the detection, three previously trained networks, VGG16, VGG19, and ResNet50, were used for the extraction of characteristics, the principal component analysis algorithm was also used to reduce dimensionality, and finally, the K-means algorithm classification. The training of the models was carried out with the use of a Kaggle open database of 7771 images which contain 38 types of diseases and healthy leaves. VGG16, VGG19, and ResNet50 were trained where the accuracy of 97.43%, 98.35%, and 98.38% was obtained. The precision obtained with the VGG16 hybrid model and the K-means algorithm was 96.26%. Therefore, the hybrid model is effective for the identification of plant diseases.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	161-166
Number of pages	6
ISBN (Electronic)	9781728176956
DOIs	https://doi.org/10.1109/CICN51697.2021.9574669
State	Published - 22 Sep 2021
Event	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Peru Duration: 22 Sep 2021 → 23 Sep 2021

Publication series

Name	Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

Conference

Conference	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
Country/Territory	Peru
City	Lima
Period	22/09/21 → 23/09/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

CN
K-means algorithm
VGG16
leaf disease
machine learning

Access to Document

10.1109/CICN51697.2021.9574669

Cite this

Mallma, J. B., Rodriguez, C., Pomachagua, Y., & Navarro, C. (2021). Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms. In Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021 (pp. 161-166). (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN51697.2021.9574669

Mallma, Joel Bejar ; Rodriguez, Ciro ; Pomachagua, Yuri et al. / Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms. Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 161-166 (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021).

@inproceedings{c8bb09dd93bd4bf38d7fb5ef8eb947b4,

title = "Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms",

abstract = "Plant leaf diseases usually affect agriculture a lot, which is one of the important sources of income for people, so diseases must be detected and recognized quickly and effectively. The research aims to identify these diseases automatically using a model based on deep learning known as convolutional neural networks and the K-means algorithm. The methodology applied for the detection, three previously trained networks, VGG16, VGG19, and ResNet50, were used for the extraction of characteristics, the principal component analysis algorithm was also used to reduce dimensionality, and finally, the K-means algorithm classification. The training of the models was carried out with the use of a Kaggle open database of 7771 images which contain 38 types of diseases and healthy leaves. VGG16, VGG19, and ResNet50 were trained where the accuracy of 97.43%, 98.35%, and 98.38% was obtained. The precision obtained with the VGG16 hybrid model and the K-means algorithm was 96.26%. Therefore, the hybrid model is effective for the identification of plant diseases.",

keywords = "CN, K-means algorithm, VGG16, leaf disease, machine learning",

author = "Mallma, {Joel Bejar} and Ciro Rodriguez and Yuri Pomachagua and Carlos Navarro",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 ; Conference date: 22-09-2021 Through 23-09-2021",

year = "2021",

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Mallma, JB, Rodriguez, C, Pomachagua, Y & Navarro, C 2021, Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms. in Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021, Institute of Electrical and Electronics Engineers Inc., pp. 161-166, 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021, Lima, Peru, 22/09/21. https://doi.org/10.1109/CICN51697.2021.9574669

Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms. / Mallma, Joel Bejar; Rodriguez, Ciro; Pomachagua, Yuri et al.
Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 161-166 (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Mallma, Joel Bejar

AU - Rodriguez, Ciro

AU - Pomachagua, Yuri

AU - Navarro, Carlos

PY - 2021/9/22

Y1 - 2021/9/22

N2 - Plant leaf diseases usually affect agriculture a lot, which is one of the important sources of income for people, so diseases must be detected and recognized quickly and effectively. The research aims to identify these diseases automatically using a model based on deep learning known as convolutional neural networks and the K-means algorithm. The methodology applied for the detection, three previously trained networks, VGG16, VGG19, and ResNet50, were used for the extraction of characteristics, the principal component analysis algorithm was also used to reduce dimensionality, and finally, the K-means algorithm classification. The training of the models was carried out with the use of a Kaggle open database of 7771 images which contain 38 types of diseases and healthy leaves. VGG16, VGG19, and ResNet50 were trained where the accuracy of 97.43%, 98.35%, and 98.38% was obtained. The precision obtained with the VGG16 hybrid model and the K-means algorithm was 96.26%. Therefore, the hybrid model is effective for the identification of plant diseases.

AB - Plant leaf diseases usually affect agriculture a lot, which is one of the important sources of income for people, so diseases must be detected and recognized quickly and effectively. The research aims to identify these diseases automatically using a model based on deep learning known as convolutional neural networks and the K-means algorithm. The methodology applied for the detection, three previously trained networks, VGG16, VGG19, and ResNet50, were used for the extraction of characteristics, the principal component analysis algorithm was also used to reduce dimensionality, and finally, the K-means algorithm classification. The training of the models was carried out with the use of a Kaggle open database of 7771 images which contain 38 types of diseases and healthy leaves. VGG16, VGG19, and ResNet50 were trained where the accuracy of 97.43%, 98.35%, and 98.38% was obtained. The precision obtained with the VGG16 hybrid model and the K-means algorithm was 96.26%. Therefore, the hybrid model is effective for the identification of plant diseases.

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M3 - Contribución a la conferencia

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BT - Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021

Y2 - 22 September 2021 through 23 September 2021

ER -

Mallma JB, Rodriguez C, Pomachagua Y, Navarro C. Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms. In Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 161-166. (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021). doi: 10.1109/CICN51697.2021.9574669

Leaf Disease Identification Using Model Hybrid Based on Convolutional Neuronal Networks and K-Means Algorithms

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