Plant Disease Detection using Image Processing

Anupama Mishra; Priyanka Chaurasia; Varsha Arya; Francisco  Jos´e Garc´ıa Pe˜nalvo

doi:10.1007/978-3-031-22018-0_21

Plant Disease Detection using Image Processing

Anupama Mishra
, Priyanka Chaurasia
, Varsha Arya
, Francisco Jos´e Garc´ıa Pe˜nalvo

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

3 Citations (Scopus)

812 Downloads (Pure)

Abstract

Plant diseases have an impact on the development of their particular species, hence early detection is crucial. Numerous Machine Learning (ML) models have been used for the identification and classification of plant diseases, this field of study now appears to have significant potential for improved accuracy. In order to identify and categorise the signs of plant diseases, numerous developed/modified ML architectures are used in conjunction with a number of visualisation techniques. Additionally, a number of performance indicators are employed to assess these structures and methodologies.

Original language	English
Title of host publication	International Conference on Cyber Security, Privacy and Networking (ICSPN 2022)
Pages	227-235
Number of pages	9
ISBN (Electronic)	978-3-031-22018-0
DOIs	https://doi.org/10.1007/978-3-031-22018-0_21
Publication status	Published online - 21 Feb 2023
Event	International Conference on Cyber Security, Privacy and Networking - Bangkok, Thailand Duration: 9 Sept 2022 → 11 Sept 2022 https://cyber-conf.com/icspn2022/

Publication series

Name	Lecture Notes in Networks and Systems
Volume	599 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Cyber Security, Privacy and Networking
Abbreviated title	ICSPN 2022
Country/Territory	Thailand
City	Bangkok
Period	9/09/22 → 11/09/22
Internet address	https://cyber-conf.com/icspn2022/

Bibliographical note

Funding Information:
Acknowledgement. This research was partially funded by the Spanish Government Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

Funding Information: Acknowledgement. This research was partially funded by the Spanish Government Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00). Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Plant disease, Machine learning
Plant disease
Machine learning

Access to Document

10.1007/978-3-031-22018-0_21

Plant Disease Detection using Image ProcessingAuthor Accepted Manuscript, 1.18 MB

Cite this

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title = "Plant Disease Detection using Image Processing",

abstract = "Plant diseases have an impact on the development of their particular species, hence early detection is crucial. Numerous Machine Learning (ML) models have been used for the identification and classification of plant diseases, this field of study now appears to have significant potential for improved accuracy. In order to identify and categorise the signs of plant diseases, numerous developed/modified ML architectures are used in conjunction with a number of visualisation techniques. Additionally, a number of performance indicators are employed to assess these structures and methodologies.",

keywords = "Plant disease, Machine learning, Plant disease, Machine learning",

author = "Anupama Mishra and Priyanka Chaurasia and Varsha Arya and \{Jos´e Garc´ıa Pe˜nalvo\}, Francisco",

note = "Funding Information: Acknowledgement. This research was partially funded by the Spanish Government Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00). Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Cyber Security, Privacy and Networking , ICSPN 2022 ; Conference date: 09-09-2022 Through 11-09-2022",

year = "2023",

month = feb,

day = "21",

doi = "10.1007/978-3-031-22018-0\_21",

language = "English",

isbn = "978-3-031-22017-3",

series = "Lecture Notes in Networks and Systems",

pages = "227--235",

booktitle = "International Conference on Cyber Security, Privacy and Networking (ICSPN 2022)",

url = "https://cyber-conf.com/icspn2022/",

}

Mishra, A, Chaurasia, P, Arya, V & Jos´e Garc´ıa Pe˜nalvo, F 2023, Plant Disease Detection using Image Processing. in International Conference on Cyber Security, Privacy and Networking (ICSPN 2022) . Lecture Notes in Networks and Systems, vol. 599 LNNS, pp. 227-235, International Conference on Cyber Security, Privacy and Networking , Bangkok, Thailand, 9/09/22. https://doi.org/10.1007/978-3-031-22018-0_21

TY - GEN

T1 - Plant Disease Detection using Image Processing

AU - Mishra, Anupama

AU - Chaurasia, Priyanka

AU - Arya, Varsha

AU - Jos´e Garc´ıa Pe˜nalvo, Francisco

N1 - Funding Information: Acknowledgement. This research was partially funded by the Spanish Government Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00). Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023/2/21

Y1 - 2023/2/21

N2 - Plant diseases have an impact on the development of their particular species, hence early detection is crucial. Numerous Machine Learning (ML) models have been used for the identification and classification of plant diseases, this field of study now appears to have significant potential for improved accuracy. In order to identify and categorise the signs of plant diseases, numerous developed/modified ML architectures are used in conjunction with a number of visualisation techniques. Additionally, a number of performance indicators are employed to assess these structures and methodologies.

AB - Plant diseases have an impact on the development of their particular species, hence early detection is crucial. Numerous Machine Learning (ML) models have been used for the identification and classification of plant diseases, this field of study now appears to have significant potential for improved accuracy. In order to identify and categorise the signs of plant diseases, numerous developed/modified ML architectures are used in conjunction with a number of visualisation techniques. Additionally, a number of performance indicators are employed to assess these structures and methodologies.

KW - Plant disease, Machine learning

KW - Plant disease

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85149661008

U2 - 10.1007/978-3-031-22018-0_21

DO - 10.1007/978-3-031-22018-0_21

M3 - Conference contribution

SN - 978-3-031-22017-3

T3 - Lecture Notes in Networks and Systems

SP - 227

EP - 235

BT - International Conference on Cyber Security, Privacy and Networking (ICSPN 2022)

T2 - International Conference on Cyber Security, Privacy and Networking

Y2 - 9 September 2022 through 11 September 2022

ER -

Plant Disease Detection using Image Processing

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

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Cite this