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

Summary

This study proposes a comprehensive data generation strategy combining traditional augmentation, CutMix, MixUp, and DCGAN synthetic images, and evaluates its impact on olive tree disease classification using EfficientNetV2B0 under three data distribution scenarios, showing substantial accuracy improvement.

SourcearXiv AIAuthor: Hicham El Akhal, Abderrahim Waga, Aissa Ben Yahya, Abdelbaki El Belrhiti El Alaoui
Enhancing Deep Learning Model Performance through Data Generation Strategies: Classification of Olive Tree Diseases
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[Submitted on 26 Oct 2026]

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

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Abstract:Deep learning models are highly effective in a wide range of domains, particu larly 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. First, we apply various data generation techniques, including traditional augmentation methods, CutMix, MixUp, and synthetic image generation using Deep Convolu tional 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. Third, we evaluate the impact of these data distributions (S1, S2, S3) on model perfor mance using the EfficientNetV2B0 architecture with pre-trained and fine-tuned weights. The experimental results show a substantial improvement in classifica tion accuracy, emphasizing the critical role of data diversity and class balance. These findings highlight the effectiveness and adaptability of the proposed strat egy of data generation and distribution for enhancing dataset quality, offering promising applications in plant disease detection and precision agriculture.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2610.35984 [cs.AI]

(or arXiv:2610.35984v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.35984

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Abderrahim Waga [view email] [v1] Mon, 26 Oct 2026 11:53:49 UTC (2,970 KB)

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Key points

  • Uses multiple data generation techniques: traditional augmentation, CutMix, MixUp, and DCGAN-based synthetic images.
  • Explores three data distribution scenarios: balancing original classes, increasing samples per class, and expanding dataset while maintaining balance.
  • Evaluates EfficientNetV2B0 with pre-trained and fine-tuned weights, demonstrating significant gains in classification accuracy.
  • Highlights the critical role of data diversity and class balance, with applications in plant disease detection and precision agriculture.

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