@inproceedings{fb822ca4773f44e491b1a96fd75b0eb8,
title = "DSC-T-Yolo-Rice: A Sand Clock Yolo Model for Rice Leaves Diseases Detection",
abstract = "In this study, a new Deep Sand Clock Tiny-Yolo Rice (DSC-T-yolo-Rice) network is proposed for rice leaf disease detection. Based on Tiny Yolo v4 network, the following modules are accomplished to improve the accuracy such as the Spatial Pyramid Pooling module (SPP), Convolutional Block Attention Module (CBAM), and Deep Sand Clock Feature Extraction Module (DSCFEM). Through the experimental results, it is proved that the proposed method achieves the highest test mAP.In addition, the proposed architecture in this paper focuses on the detection and analysis of key features.",
keywords = "component, formatting, insert, style, styling",
author = "Yu, {Fan Nong} and Shen, {Wan Chi} and Sangaiah, {Arun Kumar} and Lin, {Yi Bing}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Consumer Electronics, ICCE 2024 ; Conference date: 06-01-2024 Through 08-01-2024",
year = "2024",
doi = "10.1109/ICCE59016.2024.10444270",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE International Conference on Consumer Electronics, ICCE 2024",
address = "United States",
}