Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions DOI Creative Commons

Nijhum Paul,

G C Sunil,

David J. Horvath

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109734 - 109734

Published: Dec. 13, 2024

Language: Английский

Attention-Enhanced Corn Disease Diagnosis Using Few-Shot Learning and VGG16 DOI Creative Commons
Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda

et al.

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103172 - 103172

Published: Jan. 16, 2025

Language: Английский

Citations

1
Rapid and real time detection of black tea rolling quality by using an inexpensive machine vison system DOI
Shuai Shen, Na Ren, Hang Zheng

et al.

Food Research International, Journal Year: 2025, Volume and Issue: 205, P. 115983 - 115983

Published: Feb. 10, 2025

Language: Английский

Citations

0
Classification of maize leaf diseases with deep learning: Performance evaluation of the proposed model and use of explicable artificial intelligence DOI
Feyyaz Alpsalaz, Yıldırım ÖZÜPAK, Emrah Aslan

et al.

Chemometrics and Intelligent Laboratory Systems, Journal Year: 2025, Volume and Issue: 262, P. 105412 - 105412

Published: April 23, 2025

Language: Английский

Citations

0
Smart Grid Stability Prediction Using Adaptive Aquila Optimizer and Ensemble Stacked BiLSTM DOI Creative Commons
Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103261 - 103261

Published: Oct. 1, 2024

Language: Английский

Citations

3
Local interpretable model-agnostic explanation approach for medical imaging analysis: A systematic literature review DOI Creative Commons
Shahab Ul Hassan, Said Jadid Abdulkadir, Mohd Soperi Mohd Zahid

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 185, P. 109569 - 109569

Published: Dec. 19, 2024

Language: Английский

Citations

2
Stair image segmentation detection based on deep learning DOI

Songyang Liu,

Langhuan Sun,

Jiayu Fang

et al.

Published: July 19, 2024

Language: Английский

Citations

0
Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers DOI Creative Commons
Kunpeng Cui,

Jianbo Huang,

Guowei Dai

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2605 - 2605

Published: Nov. 4, 2024

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction large dataset requirements. overcomes these by using multi-scale weighted aggregation interaction module, enhancing both global extraction. The method refines training process, boosting accuracy robustness. With 8226 images, achieved classification 96.58%, an F1 score 96.10%, Matthews Correlation Coefficient (MCC) 92.24%, outperforming other models. inclusion Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced capture. Ablation studies revealed contributed to 0.56% improvement 0.34% increase in MCC. In summary, presents innovative solution disease diagnosis, potential applications broader agricultural detection, ultimately aiding precision agriculture health management.

Language: Английский

Citations

0
Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions DOI Creative Commons

Nijhum Paul,

G C Sunil,

David J. Horvath

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109734 - 109734

Published: Dec. 13, 2024

Language: Английский

Citations

0