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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 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 languageEnglish
Title of host publication8th International Conference on Engineering and Emerging Technologies, ICEET 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665491068
DOIs
StatePublished - 2022
Event8th International Conference on Engineering and Emerging Technologies, ICEET 2022 - Kuala Lumpur, Malaysia
Duration: 27 Oct 202228 Oct 2022

Publication series

Name8th International Conference on Engineering and Emerging Technologies, ICEET 2022

Conference

Conference8th International Conference on Engineering and Emerging Technologies, ICEET 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period27/10/2228/10/22

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

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