| Item type |
文献 / Documents(1) |
| 公開日 |
2024-01-29 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/agriculture13051066 |
|
|
言語 |
ja |
|
|
関連名称 |
10.3390/agriculture13051066 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism |
|
言語 |
en |
| 著者 |
Ni, Hongjun
Shi, Zhiwei
カルンガル, スティフィン ギディンシ
Lv, Shuaishuai
Li, Xiaoyuan
Wang, Xingxing
Zhang, Jiaqiao
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occurrences of specific pests and diseases lead to uneven dataset samples and similar early-stage symptoms, posing challenges for effective identification methods. In this study, we employ four image enhancement techniques—flipping, modifying saturation, modifying contrast, and adding blur—to balance dataset samples throughout the classification process. Simultaneously, we enhance the basic RepVGG model by incorporating the ECA attention mechanism within the Block and after the Head, resulting in the proposal of a new classification model, RepVGG_ECA. The model successfully classifies six categories: five types of typical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%, outperforming ResNet34, ResNeXt50, Shufflenet V2, and the basic RepVGG by 1.85%, 1.18%, 3.39%, and 1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification results are attained by integrating the ECA attention mechanism after the Head and within the Block of RepVGG. As a result, the classification method presented in this study provides a valuable reference for identifying typical rice pests and diseases. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
rice |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
pest and disease classification |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
ECA |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
attention mechanism |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
deep learning |
| 書誌情報 |
en : Agriculture
巻 13,
号 5,
p. 1066,
発行日 2023-05-16
|
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
20770472 |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| EID |
|
|
識別子 |
396489 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
agriculture_13_5_1066.pdf (3.6 MB)
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