Evaluating Data Augmentation Techniques for Coffee Leaf Disease
  Classification
Adrian Gheorghiu Iulian-Marius Tăiatu Dumitru-Clementin Cercel Iuliana Marin Florin Pop
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.