基于三维重建的多角度葡萄叶病害识别方法研究

为了解决葡萄在生长过程中因病害侵袭导致品质和产量下降的问题，提出了基于三维重建的多角度图像识别模型。该模型通过三维建模技术对数据进行增强，并扩充数据集用于特征辅助训练，最后与卷积神经网络相结合实现对葡萄叶片病害的识别。在测试集上，训练的3D-MobileNet、3D-Darknet53、3D-resnet34和3D-Resnet101模型相比原模型对葡萄叶片病害识别的准确率分别提高了7.2%、9.6%、10.2%、19.1%。结果表明，提出的基于三维的多角度葡萄叶片病害识别方法能够有效识别葡萄叶病害，为实现葡萄病害的自动识别提供参考。

Research on Multi-angle Identification of Grape Leaf Disease Based on 3D Reconstruction

In order to solve the problem of quality and yield decline caused by disease invasion in grape growth， this paper presented a multi-angle image recognition model based on three-dimensional. The model enhanced the data and expanded the dataset for feature aided training by 3D modeling technology. Finally， it combined with convolutional neural network to realize the identification of grape leaf diseases. The accuracy of 3D-MobileNet， 3D-Darknet53， 3D-Resnet34 and 3D-Resnet101 models increased 7.2%， 9.6%， 10.2% and 19.1% than the original model， respectively. The results showed that the method based on 3D multi-angle grape leaf disease identification could effectively identify grape disease types， and provided reference for automatic recognition of grape diseases.