ArticleFrontiers in plant science2026
FedPome: federated deep learning for real-time pomegranate disease classification.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pomegranate cultivation faces significant productivity losses due to fungal and bacterial diseases, yet existing automated detection systems rely on centralized deep learning pipelines that require raw image data aggregation, raising data privacy concerns in distributed agricultural settings. This paper proposes a federated learning (FL) framework for raw-data-decentralized pomegranate disease classification, systematically evaluating six architecturally diverse deep learning models such as Custom CNN, ResNet50, ConvNeXt_V2, ViT-B/16, EfficientNetV2-S, and MobileViT-S (under the FedAvg aggregation protocol across five simulated clients and 10 communication rounds). All six architectures are initialized with publicly available ImageNetpretrained weights to ensure a fair architectural comparison. Experiments are conducted on a working set of 6,823 images derived from the Mendeley Pomegranate Fruit Diseases Dataset (5,099 original images) spanning five disease classes-
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