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References (27)

[1]
LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification
2026Md. Ehsanul Haque, Fahmid Al Farid et al.
[2]
An attention enhanced CNN ensemble for interpretable and accurate cotton leaf disease classification
2026Md. Ehsanul Haque, Md Tamim Hasan Saykat et al.
[3]
Lightweight grape leaf disease recognition method based on transformer framework
2025Ning Zhang, Enxu Zhang et al.
[4]
The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI
2025Fatih Atesoglu, Harun Bingol
[5]
Integrating CBAM and Squeeze‐and‐Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis
2025Yavuz Unal
[6]
Grapes leaf disease dataset for precision agriculture
2025Madhuri Dharrao, N. Zade et al.
[7]
Artificial Intelligence and Plant Disease Management: An Agro‐Innovative Approach
2025Kritika Minhans, Sushma Sharma et al.
[8]
Applications of AI in precision agriculture
2025Garima Gupta, Sudhir Kumar Pal
[9]
Grape leaf disease detection using deep learning approach
2025Savitha Shetty, Saritha Shetty
[10]
A Comparative Study and Optimization of Deep Learning Models for Grape Leaf Disease Identification
2024Rasika Gajendra Patil, Ajit More
[11]
A lightweight and efficient model for grape bunch detection and biophysical anomaly assessment in complex environments based on YOLOv8s
2024Wenji Yang, Xiaoying Qiu
[12]
Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM
2024Md. Jawadul Karim, Md. Omaer Faruq Goni et al.
[13]
Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier
2024K. Prasad, H. Vaidya et al.
[14]
Grapevine fruits disease detection using different deep learning models
2024Om G, Saketh Ram Billa et al.
[15]
Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases
2024Ismail Kunduracioglu, Ishak Paçal
[16]
Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions
2023Isabel Pinheiro, Germano Moreira et al.
[17]
Evolution of the wine market in Europe: trends and barriers in the context of the COVID-19 pandemic
2022Iulia Ruxandra Țicău
[18]
GrapeGAN: Unsupervised image enhancement for improved grape leaf disease recognition
2022Haibin Jin, Yue Li et al.
[19]
GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases
2022Jianwu Lin, Xiaoyulong Chen et al.
[20]
Deep Learning Based Automatic Grape Downy Mildew Detection
2022Zhao Zhang, Yongliang Qiao et al.

Showing 20 of 27 references

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"Optimized DenseNet-121 framework for real-time, explainable grape leaf disease classification."

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5

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