arXiv:2610.35984v1 — data-generation strategies improve EfficientNetV2B0 classification of olive tree diseases
This arXiv preprint (2610.35984v1) evaluates a comprehensive data-generation pipeline — traditional augmentation, CutMix, MixUp, and synthetic images from a DCGAN — used in three distribution scenarios (balancing classes, increasing per-class samples, and expanding while keeping classes balanced). The authors report substantial accuracy gains when training a pre-trained and fine-tuned EfficientNetV2B0 on the augmented olive tree disease dataset, arguing the approach improves dataset quality for plant disease detection.