基于生成人工智能（GenAI）的水稻（Citrullus lanatus）病害分类——通过自定义 EfficientNetV2-L 模型结合合成图像与实地图像

当前生成人工智能（GenAI）模型的发展为生成高分辨率合成图像提供了新可能，从而为农业领域的计算机视觉模型训练提供了具有前景的替代方案。 在作物病害诊断领域，GenAI 模型正被用于创建各种疾病的合成图像， potentially facilitate model creation and reduce 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 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.