Abstract
The current advancements in generative artificial intelligence (GenAI) models have paved the way for new possibilities for generating high-resolution synthetic images, thereby offering a promising alternative to traditional image acquisition for training computer vision models in agriculture. In the context of crop disease diagnosis, GenAI models are being used to create synthetic images of various diseases, potentially facilitating model creation and reducing the dependency on resource-intensive in-field data collection. However, limited research has been conducted on evaluating the effectiveness of integrating real with synthetic images to improve disease classification performance. Therefore, this study aims to investigate whether combining a limited number of real images with synthetic images can enhance the prediction accuracy of an EfficientNetV2-L model for classifying watermelon \textit{(Citrullus lanatus)} diseases. The training dataset was divided into five treatments: H0 (only real images), H1 (only synthetic images), H2 (1:1 real-to-synthetic), H3 (1:10 real-to-synthetic), and H4 (H3 + random images to improve variability and model generalization). All treatments were trained using a custom EfficientNetV2-L architecture with enhanced fine-tuning and transfer learning techniques. Models trained on H2, H3, and H4 treatments demonstrated high precision, recall, and F1-score metrics. Additionally, the weighted F1-score increased from 0.65 (on H0) to 1.00 (on H3-H4) signifying that the addition of a small number of real images with a considerable volume of synthetic images improved model performance and generalizability. Overall, this validates the findings that synthetic images alone cannot adequately substitute for real images; instead, both must be used in a hybrid manner to maximize model performance for crop disease classification.
Abstract (translated)
在生成式人工智能（GenAI）模型的最新进展推动下，高分辨率合成图像的生成已成为可能。这为农业中训练计算机视觉模型提供了传统图像获取方法之外的一个有前景的选择。特别是在作物病害诊断领域，GenAI 模型被用来创建各种疾病的合成图像，这有可能促进模型创建并减少对资源密集型实地数据收集的依赖。然而，关于将真实与合成图像相结合以提高疾病分类性能的有效性的研究却相对较少。 因此，本研究旨在调查使用少量的真实图像和大量合成图像结合是否可以提升EfficientNetV2-L模型在西瓜（Citrullus lanatus）病害分类上的预测精度。训练数据集被划分为五种处理方式：H0（仅真实图像）、H1（仅合成图像）、H2（真实与合成图像比例为1:1）、H3（真实与合成图像比例为1:10），以及H4（在H3基础上加入随机图像以提高多样性和模型泛化能力）。所有处理方式均使用增强的微调和迁移学习技术训练了自定义的EfficientNetV2-L架构。结果显示，采用H2、H3及H4处理方式训练出的模型具有高精度、召回率和F1评分等指标。另外，在这些处理下加权F1评分从0.65（仅使用真实图像时）提高到了1.00（在使用了少量的真实图像和大量合成图像的情况下），这表明，适量的真实图像与大量的合成图像是提升模型性能和泛化能力的关键。 总的来说，这项研究证明了单纯依靠合成图像无法充分替代真实图像；相反，两种方式的混合使用可以最大化地提高作物病害分类中的模型性能。
URL
https://arxiv.org/abs/2508.10156
