近年来，深度学习极大地改善了植物中各种疾病的识别和诊断。在本报告中，我们使用单叶图像调查病理学分类问题。..

Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures for Plant Pathology Classification

In recent years, deep learning has vastly improved the identification and diagnosis of various diseases in plants. In this report, we investigate the problem of pathology classification using images of a single leaf.We explore the use of standard benchmark models such as VGG16, ResNet101, and DenseNet 161 to achieve a 0.945 score on the task. Furthermore, we explore the use of the newer EfficientNet model, improving the accuracy to 0.962. Finally, we introduce the state-of-the-art idea of semi-supervised Noisy Student training to the EfficientNet, resulting in significant improvements in both accuracy and convergence rate. The final ensembled Noisy Student model performs very well on the task, achieving a test score of 0.982.