Enhancing Deep Learning Model Performance through Data Generation Strategies: Classification of Olive Tree Diseases

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Computer Science > Artificial Intelligence arXiv:2610.35984 (cs) [Submitted on 26 Oct 2026] Title: Enhancing Deep Learning Model Performance through Data Generation Strategies: Classification of Olive Tree Diseases Authors: Hicham El Akhal, Abderrahim Waga, Aissa Ben Yahya, Abdelbaki El Belrhiti El Alaoui.

计算机科学 > 人工智能 arXiv:2610.35984 (cs) [提交于 2026 年 10 月 26 日] 标题：通过数据生成策略增强深度学习模型性能：橄榄树病害分类作者：Hicham El Akhal, Abderrahim Waga, Aissa Ben Yahya, Abdelbaki El Belrhiti El Alaoui。

Abstract: Deep learning models are highly effective in a wide range of domains, particularly in plant disease diagnosis. However, their performance is often limited by challenges such as insufficient data and class imbalance. In our work, we propose a comprehensive data generation strategy to overcome these limitations.

摘要：深度学习模型在广泛的领域中非常有效，特别是在植物病害诊断方面。然而，它们的性能往往受到数据不足和类别不平衡等挑战的限制。在我们的工作中，我们提出了一种全面的数据生成策略来克服这些限制。

First, we apply various data generation techniques, including traditional augmentation methods, CutMix, MixUp, and synthetic image generation using Deep Convolutional Generative Adversarial Networks (DCGANs), to create new data samples. Second, we exploit this generated data in three scenarios: (S1) balancing class distribution in the original dataset, (S2) increasing the number of samples per class, and (S3) expanding the dataset while maintaining balanced classes.

首先，我们应用各种数据生成技术，包括传统的增强方法、CutMix、MixUp 以及使用深度卷积生成对抗网络 (DCGAN) 进行合成图像生成，以创建新的数据样本。其次，我们在三种场景中利用这些生成的数据：(S1) 平衡原始数据集中的类别分布，(S2) 增加每个类别的样本数量，以及 (S3) 在保持类别平衡的同时扩展数据集。

Third, we evaluate the impact of these data distributions (S1, S2, S3) on model performance using the EfficientNetV2B0 architecture with pre-trained and fine-tuned weights. The experimental results show a substantial improvement in classification accuracy, emphasizing the critical role of data diversity and class balance.

第三，我们使用具有预训练和微调权重的 EfficientNetV2B0 架构，评估了这些数据分布 (S1, S2, S3) 对模型性能的影响。实验结果显示分类准确率有了显著提高，强调了数据多样性和类别平衡的关键作用。

These findings highlight the effectiveness and adaptability of the proposed strategy of data generation and distribution for enhancing dataset quality, offering promising applications in plant disease detection and precision agriculture.

这些发现突显了所提出的数据生成和分布策略在提高数据集质量方面的有效性和适应性，为植物病害检测和精准农业提供了广阔的应用前景。