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

Summary:This research proposes a comprehensive data generation strategy to address challenges faced by deep learning models in plant disease diagnosis, such as insufficient data and class imbalance. The team employs various data generation techniques, including traditional augmentation methods, CutMix, MixUp, and synthetic image generation using DCGANs. They apply these techniques in three scenarios: balancing class distribution, increasing the number of samples per class, and expanding the dataset whil

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Background and Motivation

Deep learning models are increasingly applied in domains like plant disease diagnosis, but their performance is often limited by insufficient data and class imbalance. To address these challenges, this study proposes a comprehensive data generation strategy.

Data Generation Strategy

1. Techniques: The team employs various data generation methods, including traditional data augmentation (e.g., rotation, flipping, scaling), CutMix, MixUp, and synthetic image generation using Deep Convolutional Generative Adversarial Networks (DCGANs).
2. Scenarios:
   • S1: Balancing class distribution in the original dataset.
   • S2: Increasing the number of samples per class.
   • S3: Expanding the dataset while maintaining balanced classes.

Experiments and Results

The study uses the EfficientNetV2B0 architecture to evaluate pre-trained and fine-tuned weights. The results show a significant improvement in classification accuracy through the proposed data generation strategies. Specifically:

• S1 Scenario: The model's ability to recognize minority classes is significantly enhanced when class distribution is balanced.
• S2 Scenario: Increasing the number of samples boosts the overall performance, especially for data-sparse classes.
• S3 Scenario: Expanding the dataset while maintaining balance further improves the model's generalization capabilities.

Engineering Trade-offs and Practical Applications

• Advantages:
  • The data generation strategy effectively alleviates the problem of insufficient data.
  • It enhances the model's ability to handle unbalanced data.
• Challenges:
  • The data generation process increases computational overhead.
  • The strategy needs to be adjusted for different application scenarios.
• Recommendations:
  • In resource-constrained environments, lightweight data augmentation methods should be prioritized.
  • For complex scenarios, combining multiple data generation techniques is recommended for optimal results.

Conclusion

The proposed data generation strategy provides a new approach to applying deep learning models in plant disease diagnosis, emphasizing the critical role of data diversity and class balance in enhancing model performance.

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Source: ArXiv AI (cs.AI) (2026-10-29)

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Tags: #Data Generation Follow #Deep Learning Follow #Plant Disease Diagnosis Follow #Model Optimization Follow #Precision Agriculture Follow