Federated learning-based UAVs for the diagnosis of Plant Diseases

Fawad Salam Khan; Sikandar Khan; Mohd Norzali Haji Mohd; Athar Waseem; Muhammad Numan Ali Khan; Sajid Ali; Rizwan Ahmed

doi:10.1109/ICEET56468.2022.10007133

Federated learning-based UAVs for the diagnosis of Plant Diseases

Fawad Salam Khan^*
, Sikandar Khan
, Mohd Norzali Haji Mohd
, Athar Waseem
, Muhammad Numan Ali Khan
, Sajid Ali
, Rizwan Ahmed

^*Corresponding author for this work

Department of Mechanical Engineering

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

34 Scopus citations

Abstract

The technological revolution for farmers, especially for the safety of their crops from pests, plays an evident change and convenience for the agriculture industry. The current research presented the classification of different pests using federated learning-based UAVs. The designed scenarios comprise four different sites connected with a global model where different parameters for these sites are received from the local model. State-of-The-Art EfficientNet deep model with B03 configurations provides the best accuracy for classifying nine types of pests. The system can achieve an accuracy of 99.55% with the augmentation of images into different angles. The federated learning designed UAVs are the most reliable connection with very less computation power during the classification of pests for the agricultural environment.

Original language	English
Title of host publication	8th International Conference on Engineering and Emerging Technologies, ICEET 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665491068
DOIs	https://doi.org/10.1109/ICEET56468.2022.10007133
State	Published - 2022
Event	8th International Conference on Engineering and Emerging Technologies, ICEET 2022 - Kuala Lumpur, Malaysia Duration: 27 Oct 2022 → 28 Oct 2022

Publication series

Name	8th International Conference on Engineering and Emerging Technologies, ICEET 2022

Conference

Conference	8th International Conference on Engineering and Emerging Technologies, ICEET 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	27/10/22 → 28/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Agriculture
Deep Learning
Federated Learning
Pests
UAVs

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Media Technology

UN SDGs

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

Access to Document

10.1109/ICEET56468.2022.10007133

Cite this

Khan, F. S., Khan, S., Mohd, M. N. H., Waseem, A., Khan, M. N. A., Ali, S., & Ahmed, R. (2022). Federated learning-based UAVs for the diagnosis of Plant Diseases. In 8th International Conference on Engineering and Emerging Technologies, ICEET 2022 (8th International Conference on Engineering and Emerging Technologies, ICEET 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEET56468.2022.10007133

@inproceedings{7b6ba38e723548708cc5730361bbc41d,

title = "Federated learning-based UAVs for the diagnosis of Plant Diseases",

abstract = "The technological revolution for farmers, especially for the safety of their crops from pests, plays an evident change and convenience for the agriculture industry. The current research presented the classification of different pests using federated learning-based UAVs. The designed scenarios comprise four different sites connected with a global model where different parameters for these sites are received from the local model. State-of-The-Art EfficientNet deep model with B03 configurations provides the best accuracy for classifying nine types of pests. The system can achieve an accuracy of 99.55\% with the augmentation of images into different angles. The federated learning designed UAVs are the most reliable connection with very less computation power during the classification of pests for the agricultural environment.",

keywords = "Agriculture, Deep Learning, Federated Learning, Pests, UAVs",

author = "Khan, \{Fawad Salam\} and Sikandar Khan and Mohd, \{Mohd Norzali Haji\} and Athar Waseem and Khan, \{Muhammad Numan Ali\} and Sajid Ali and Rizwan Ahmed",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 8th International Conference on Engineering and Emerging Technologies, ICEET 2022 ; Conference date: 27-10-2022 Through 28-10-2022",

year = "2022",

doi = "10.1109/ICEET56468.2022.10007133",

language = "English",

series = "8th International Conference on Engineering and Emerging Technologies, ICEET 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "8th International Conference on Engineering and Emerging Technologies, ICEET 2022",

address = "United States",

}

Khan, FS, Khan, S, Mohd, MNH, Waseem, A, Khan, MNA, Ali, S & Ahmed, R 2022, Federated learning-based UAVs for the diagnosis of Plant Diseases. in 8th International Conference on Engineering and Emerging Technologies, ICEET 2022. 8th International Conference on Engineering and Emerging Technologies, ICEET 2022, Institute of Electrical and Electronics Engineers Inc., 8th International Conference on Engineering and Emerging Technologies, ICEET 2022, Kuala Lumpur, Malaysia, 27/10/22. https://doi.org/10.1109/ICEET56468.2022.10007133

Federated learning-based UAVs for the diagnosis of Plant Diseases. / Khan, Fawad Salam; Khan, Sikandar; Mohd, Mohd Norzali Haji et al.
8th International Conference on Engineering and Emerging Technologies, ICEET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (8th International Conference on Engineering and Emerging Technologies, ICEET 2022).

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

TY - GEN

T1 - Federated learning-based UAVs for the diagnosis of Plant Diseases

AU - Khan, Fawad Salam

AU - Khan, Sikandar

AU - Mohd, Mohd Norzali Haji

AU - Waseem, Athar

AU - Khan, Muhammad Numan Ali

AU - Ali, Sajid

AU - Ahmed, Rizwan

PY - 2022

Y1 - 2022

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AB - The technological revolution for farmers, especially for the safety of their crops from pests, plays an evident change and convenience for the agriculture industry. The current research presented the classification of different pests using federated learning-based UAVs. The designed scenarios comprise four different sites connected with a global model where different parameters for these sites are received from the local model. State-of-The-Art EfficientNet deep model with B03 configurations provides the best accuracy for classifying nine types of pests. The system can achieve an accuracy of 99.55% with the augmentation of images into different angles. The federated learning designed UAVs are the most reliable connection with very less computation power during the classification of pests for the agricultural environment.

KW - Agriculture

KW - Deep Learning

KW - Federated Learning

KW - Pests

KW - UAVs

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M3 - Conference contribution

AN - SCOPUS:85146872211

T3 - 8th International Conference on Engineering and Emerging Technologies, ICEET 2022

BT - 8th International Conference on Engineering and Emerging Technologies, ICEET 2022

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 27 October 2022 through 28 October 2022

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Khan FS, Khan S, Mohd MNH, Waseem A, Khan MNA, Ali S et al. Federated learning-based UAVs for the diagnosis of Plant Diseases. In 8th International Conference on Engineering and Emerging Technologies, ICEET 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (8th International Conference on Engineering and Emerging Technologies, ICEET 2022). doi: 10.1109/ICEET56468.2022.10007133

Federated learning-based UAVs for the diagnosis of Plant Diseases

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this