The detection and classification of diseases in Robusta coffee leaves are
essential to ensure that plants are healthy and the crop yield is kept high.
However, this job requires extensive botanical knowledge and much wasted time.
Therefore, this task and others similar to it have been extensively researched
subjects in image classification. Regarding leaf disease classification, most
approaches have used the more popular PlantVillage dataset while completely
disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As
the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of
pre-trained models and multiple augmentation techniques need to be used. The
current paper uses the RoCoLe dataset and approaches based on deep learning for
classifying coffee leaf diseases from images, incorporating the pix2pix model
for segmentation and cycle-generative adversarial network (CycleGAN) for
augmentation. Our study demonstrates the effectiveness of Transformer-based
models, online augmentations, and CycleGAN augmentation in improving leaf
disease classification. While synthetic data has limitations, it complements
real data, enhancing model performance. These findings contribute to developing
robust techniques for plant disease detection and classification.

本研究使用基于深度学习的方法，结合 pix2pix 模型用于分割和 CycleGAN 用于增强，通过 RoCoLe 数据集进行咖啡叶病害的图像分类，展示了变压器模型、在线增强和 CycleGAN 增强对叶病害分类的有效性，合成数据在增强模型性能上与真实数据相辅相成，这些发现有助于发展强大的植物病害检测和分类技术。