Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features

Zakari Hassan Mohammed; Oyefolahan I. O; Mohammed D. Abdulmalik; Sulaimon A. Bashir

doi:10.1007/978-3-030-69143-1_24

Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features

Zakari Hassan Mohammed^*, Oyefolahan I. O, Mohammed D. Abdulmalik, Sulaimon A. Bashir

^*Corresponding author for this work

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

Abstract

Quantity and quality of agricultural products are significantly reduced by diseases. Identification and classification of these plant diseases using plant leaf images is one of the important agricultural areas of research for which machine-learning models can be employed. The Powdery Mildew and Bacterial Leaf Blight diseases are two common diseases that can have a severe effect on crop production. To minimize the loss incurred by Powdery Mildew and Bacterial Leaf Blight diseases and to ensure more accurate automatic detection of these pathogens, this paper proposes an approach for identifying these diseases, based on a combination of Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) features (HOG + LBP) using Naïve Bayes (NB) Classifier. The NB classifier was also trained with only the HOG features and also trained with only the LBP features. However the NB classifier trained with the HOG + LBP features obtained a higher performance accuracy of 95.45% as compared to NB classifier trained with only HOG features and NB classifier trained only with LBP features with accuracy of 90.91% and 86.36% respectively.

Original language	English
Title of host publication	Information and Communication Technology and Applications - Third International Conference, ICTA 2020, Revised Selected Papers
Editors	Sanjay Misra, Bilkisu Muhammad-Bello
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	301-314
Number of pages	14
ISBN (Print)	9783030691424
DOIs	https://doi.org/10.1007/978-3-030-69143-1_24
State	Published - 2021
Externally published	Yes
Event	3rd International Conference on Information and Communication Technology and Applications, ICTA 2020 - Virtual, Online Duration: 24 Nov 2020 → 27 Nov 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1350
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Information and Communication Technology and Applications, ICTA 2020
City	Virtual, Online
Period	24/11/20 → 27/11/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Bacterial leaf blight
Plant disease
Plant disease detection
Powdery mildew

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-69143-1_24

Cite this

Mohammed, Z. H., O, O. I., Abdulmalik, M. D., & Bashir, S. A. (2021). Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features. In S. Misra, & B. Muhammad-Bello (Eds.), Information and Communication Technology and Applications - Third International Conference, ICTA 2020, Revised Selected Papers (pp. 301-314). (Communications in Computer and Information Science; Vol. 1350). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-69143-1_24

Mohammed, Zakari Hassan ; O, Oyefolahan I. ; Abdulmalik, Mohammed D. et al. / Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features. Information and Communication Technology and Applications - Third International Conference, ICTA 2020, Revised Selected Papers. editor / Sanjay Misra ; Bilkisu Muhammad-Bello. Springer Science and Business Media Deutschland GmbH, 2021. pp. 301-314 (Communications in Computer and Information Science).

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title = "Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features",

abstract = "Quantity and quality of agricultural products are significantly reduced by diseases. Identification and classification of these plant diseases using plant leaf images is one of the important agricultural areas of research for which machine-learning models can be employed. The Powdery Mildew and Bacterial Leaf Blight diseases are two common diseases that can have a severe effect on crop production. To minimize the loss incurred by Powdery Mildew and Bacterial Leaf Blight diseases and to ensure more accurate automatic detection of these pathogens, this paper proposes an approach for identifying these diseases, based on a combination of Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) features (HOG + LBP) using Na{\"i}ve Bayes (NB) Classifier. The NB classifier was also trained with only the HOG features and also trained with only the LBP features. However the NB classifier trained with the HOG + LBP features obtained a higher performance accuracy of 95.45% as compared to NB classifier trained with only HOG features and NB classifier trained only with LBP features with accuracy of 90.91% and 86.36% respectively.",

keywords = "Bacterial leaf blight, Plant disease, Plant disease detection, Powdery mildew",

author = "Mohammed, {Zakari Hassan} and O, {Oyefolahan I.} and Abdulmalik, {Mohammed D.} and Bashir, {Sulaimon A.}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Conference on Information and Communication Technology and Applications, ICTA 2020 ; Conference date: 24-11-2020 Through 27-11-2020",

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Mohammed, ZH, O, OI, Abdulmalik, MD & Bashir, SA 2021, Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features. in S Misra & B Muhammad-Bello (eds), Information and Communication Technology and Applications - Third International Conference, ICTA 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1350, Springer Science and Business Media Deutschland GmbH, pp. 301-314, 3rd International Conference on Information and Communication Technology and Applications, ICTA 2020, Virtual, Online, 24/11/20. https://doi.org/10.1007/978-3-030-69143-1_24

Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features. / Mohammed, Zakari Hassan; O, Oyefolahan I.; Abdulmalik, Mohammed D. et al.
Information and Communication Technology and Applications - Third International Conference, ICTA 2020, Revised Selected Papers. ed. / Sanjay Misra; Bilkisu Muhammad-Bello. Springer Science and Business Media Deutschland GmbH, 2021. p. 301-314 (Communications in Computer and Information Science; Vol. 1350).

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

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AU - Abdulmalik, Mohammed D.

AU - Bashir, Sulaimon A.

PY - 2021

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AB - Quantity and quality of agricultural products are significantly reduced by diseases. Identification and classification of these plant diseases using plant leaf images is one of the important agricultural areas of research for which machine-learning models can be employed. The Powdery Mildew and Bacterial Leaf Blight diseases are two common diseases that can have a severe effect on crop production. To minimize the loss incurred by Powdery Mildew and Bacterial Leaf Blight diseases and to ensure more accurate automatic detection of these pathogens, this paper proposes an approach for identifying these diseases, based on a combination of Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) features (HOG + LBP) using Naïve Bayes (NB) Classifier. The NB classifier was also trained with only the HOG features and also trained with only the LBP features. However the NB classifier trained with the HOG + LBP features obtained a higher performance accuracy of 95.45% as compared to NB classifier trained with only HOG features and NB classifier trained only with LBP features with accuracy of 90.91% and 86.36% respectively.

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SN - 9783030691424

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Mohammed ZH, O OI, Abdulmalik MD, Bashir SA. Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features. In Misra S, Muhammad-Bello B, editors, Information and Communication Technology and Applications - Third International Conference, ICTA 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 301-314. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-69143-1_24

Identification of Bacterial Leaf Blight and Powdery Mildew Diseases Based on a Combination of Histogram of Oriented Gradient and Local Binary Pattern Features

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this