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 language | English |
|---|---|
| Title of host publication | 2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 354-358 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798350357905 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024 - Miri Sarawak, Malaysia Duration: 17 Jan 2024 → 19 Jan 2024 |
Publication series
| Name | 2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024 |
|---|
Conference
| Conference | 2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024 |
|---|---|
| Country/Territory | Malaysia |
| City | Miri Sarawak |
| Period | 17/01/24 → 19/01/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Data Augmentation
- Few-Shot Learning
- Meta-Learning
- Plant Pests and Diseases
- Transfer Learning
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