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disease apple leaf dataset  (Kaggle Inc)

 
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    Kaggle Inc disease apple leaf dataset
    Disease Apple Leaf Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/disease+apple+leaf+dataset/dataset+disease+heart/pm42156509-120-4-20
    Average 86 stars, based on 1 article reviews
    disease apple leaf dataset - by Bioz Stars, 2026-11
    86/100 stars

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    other:

    Article Title: Hybrid IGWO-Dingo optimized DeMoHybridNet model for multi-class leaf disease identification.
    Article Snippet: This work considers a 13-disease Apple leaf dataset where 12 disease conditions and 1 healthy condition are available in the Kaggle repository [34].



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    Image Search Results


    Workflow diagram of proposed Apple leaf disease detection framework.

    Journal: Frontiers in Artificial Intelligence

    Article Title: LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification

    doi: 10.3389/frai.2025.1689865

    Figure Lengend Snippet: Workflow diagram of proposed Apple leaf disease detection framework.

    Article Snippet: This research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal ( ).

    Techniques:

    Sample images from the apple leaf disease dataset.

    Journal: Frontiers in Artificial Intelligence

    Article Title: LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification

    doi: 10.3389/frai.2025.1689865

    Figure Lengend Snippet: Sample images from the apple leaf disease dataset.

    Article Snippet: This research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal ( ).

    Techniques:

    Architecture of the proposed LeafSightX model (the dual backbone feature extraction, multi-head self-attention modules, and the fusion mechanism that jointly learn discriminative representations for apple leaf disease classification).

    Journal: Frontiers in Artificial Intelligence

    Article Title: LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification

    doi: 10.3389/frai.2025.1689865

    Figure Lengend Snippet: Architecture of the proposed LeafSightX model (the dual backbone feature extraction, multi-head self-attention modules, and the fusion mechanism that jointly learn discriminative representations for apple leaf disease classification).

    Article Snippet: This research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal ( ).

    Techniques: Extraction

    Radar plot illustrating the computational cost of six deep learning models for Apple Leaf disease classification, including training time, test inference time, and per-sample inference time.

    Journal: Frontiers in Artificial Intelligence

    Article Title: LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification

    doi: 10.3389/frai.2025.1689865

    Figure Lengend Snippet: Radar plot illustrating the computational cost of six deep learning models for Apple Leaf disease classification, including training time, test inference time, and per-sample inference time.

    Article Snippet: This research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal ( ).

    Techniques:

    Confusion matrices for various deep learning models on apple leaf disease classification, showing true vs. predicted labels for five classes: Healthy leaf, Alternaria leaf spot, Rust, Gray spot, and Brown spot.

    Journal: Frontiers in Artificial Intelligence

    Article Title: LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification

    doi: 10.3389/frai.2025.1689865

    Figure Lengend Snippet: Confusion matrices for various deep learning models on apple leaf disease classification, showing true vs. predicted labels for five classes: Healthy leaf, Alternaria leaf spot, Rust, Gray spot, and Brown spot.

    Article Snippet: This research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal ( ).

    Techniques: