Reformer: Re-parameterized kernel lightweight transformer for grape disease segmentation DOI
Weisong Mu,

Zibo Feng,

Weisong Mu

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 265, P. 125757 - 125757

Published: Dec. 9, 2024

Language: Английский

A systematic review of deep learning techniques for plant diseases DOI Creative Commons
İshak Paçal, İsmail Kunduracıoğlu, Mehmet Hakkı Alma

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(11)

Published: Sept. 30, 2024

Language: Английский

Citations

15
FATDNet: A fusion adversarial network for tomato leaf disease segmentation under complex backgrounds DOI

Zaichun Yang,

Lixiang Sun, Zhihuan Liu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110270 - 110270

Published: March 20, 2025

Language: Английский

Citations

1
Recent advances in Transformer technology for agriculture: A comprehensive survey DOI
Weijun Xie,

M G Zhao,

Ying Liu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 138, P. 109412 - 109412

Published: Oct. 11, 2024

Language: Английский

Citations

6
Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model DOI
Kibitok Abraham, Mohammed Abo‐Zahhad,

Moataz M. Abdelwahab

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(4), P. 3149 - 3161

Published: May 28, 2024

Language: Английский

Citations

4
MFHSformer: Hierarchical sparse transformer based on multi-feature fusion for soil pore segmentation DOI

Hao Bai,

Qiaoling Han,

Yandong Zhao

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 272, P. 126789 - 126789

Published: Feb. 7, 2025

Language: Английский

Citations

0
A deep learning-based micro-CT image analysis pipeline for nondestructive quantification of the maize kernel internal structure DOI Creative Commons
Juan Wang, Si Yang,

Chuanyu Wang

et al.

Plant Phenomics, Journal Year: 2025, Volume and Issue: unknown, P. 100022 - 100022

Published: Feb. 1, 2025

Language: Английский

Citations

0
CMAA: Channel-wise multi-scale adaptive attention network for metallographic image semantic segmentation DOI

Yongliang Sun,

Xiangyang Huang

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126925 - 126925

Published: March 1, 2025

Language: Английский

Citations

0
An enhanced vision transformer network for efficient and accurate crop disease detection DOI
Md. Ashraful Haque, Chandan Kumar Deb, Pushkar Gole

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127743 - 127743

Published: April 1, 2025

Language: Английский

Citations

0
Robust crop disease detection using Multi-Domain Data Augmentation and Isolated Test-Time Adaptation DOI Creative Commons
Rui Fu, Han Jiao,

Yumei Sun

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127324 - 127324

Published: April 1, 2025

Language: Английский

Citations

0
A dual-branch model combining convolution and vision transformer for crop disease classification DOI Creative Commons

Qingduan Meng,

Guo Jia-dong,

Hui Zhang

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0321753 - e0321753

Published: April 24, 2025

Computer vision holds tremendous potential in crop disease classification, but the complex texture and shape characteristics of diseases make classification challenging. To address these issues, this paper proposes a dual-branch model for which combines Convolutional Neural Network (CNN) with Vision Transformer (ViT). Here, convolutional branch is utilized to capture local features while handle global features. A learnable parameter used achieve linear weighted fusion two types An Aggregated Local Perceptive Feed Forward Layer (ALP-FFN) introduced enhance model’s representation capability by introducing locality into encoder. Furthermore, constructs lightweight block using ALP-FFN self-attention mechanism reduce parameters computational cost. The proposed achieves an exceptional accuracy 99.71% on PlantVillage dataset only 4.9M 0.62G FLOPs, surpassing state-of-the-art TNT-S (accuracy: 99.11%, parameters: 23.31M, FLOPs: 4.85G) 0.6%. On Potato Leaf dataset, attains 98.78% accuracy, outperforming advanced ResNet-18 98.05%, 11.18M, 1.82G) 0.73%. effectively advantages CNN ViT maintaining design, providing effective method precise identification diseases.

Language: Английский

Citations

0