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

A study posted to arXiv (2610.35984v1) reports that a combined data-generation strategy substantially improved classification accuracy for olive tree diseases using the EfficientNetV2B0 architecture with pre-trained and fine-tuned weights. The researchers applied traditional augmentation, CutMix, MixUp and synthetic image generation with Deep Convolutional Generative Adversarial Networks (DCGANs), then tested the generated data in three scenarios: balancing class distribution in the original dataset (S1), increasing samples per class (S2), and expanding the dataset while keeping classes balanced (S3). The authors attribute the accuracy gain to data diversity and class balance, positioning the approach for plant disease detection and precision agriculture.

arXiv:2610.35984v1 Announce Type: new 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.