Advancements for Improved Plant Disease and Pest Identification: A Survey

Tang Feilong, Hoe Tung Yew, Farrah Wong, Rosalyn R. Porle

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

1 Scopus citations

Abstract

Image classification methods are widely applied to the identification of plant pests and diseases. Despite recent innovative efforts in plant pest and disease identification, the vast diversity of plant pest and disease types and long-tail distribution of data continue to pose serious challenges. In this context, we have thoroughly reviewed numerous papers published over the past three years regarding plant pest and disease identification and classification tasks. Our aim is to provide a timely yet not exhaustive overview of the latest advancements in these tasks, and fairly compare the strengths and weaknesses of existing work. To enrich this survey, we provide in-depth analysis and thoughtful discussion on these topics in each subsection, in the hope of providing guidance for subsequent research.

Original languageEnglish
Title of host publication2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages354-358
Number of pages5
ISBN (Electronic)9798350357905
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024 - Miri Sarawak, Malaysia
Duration: 17 Jan 202419 Jan 2024

Publication series

Name2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

Conference

Conference2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024
Country/TerritoryMalaysia
CityMiri Sarawak
Period17/01/2419/01/24

Keywords

  • Data Augmentation
  • Few-Shot Learning
  • Meta-Learning
  • Plant Pests and Diseases
  • Transfer Learning

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