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The paper proposes a comprehensive data generation strategy combining traditional augmentation, CutMix, MixUp, and DCGAN-generated images to address data insufficiency and class imbalance for olive tree disease classification, evaluated using EfficientNetV2B0 and showing notable accuracy improvements.