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.

翻译：检测并分类罗布斯塔咖啡叶病害对于确保植株健康及维持高产至关重要。然而，这项工作需要广泛的植物学知识且耗费大量时间。因此，该任务及其他类似任务已成为图像分类领域广泛研究的课题。在叶片病害分类中，大多数方法采用更流行的PlantVillage数据集，而完全忽略了其他数据集，如罗布斯塔咖啡叶（RoCoLe）数据集。由于RoCoLe数据集存在类别不平衡且样本数量有限，需使用预训练模型微调及多种增强技术。本文基于RoCoLe数据集，采用深度学习方法对咖啡叶病害图像进行分类，并结合pix2pix模型进行分割、循环生成对抗网络（CycleGAN）进行数据增强。研究表明，基于Transformer的模型、在线增强及CycleGAN增强在提升叶片病害分类性能方面具有有效性。尽管合成数据存在局限性，但它能补充真实数据，增强模型表现。这些发现为开发鲁棒的植物病害检测与分类技术提供了参考。