Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification

Adrian Gheorghiu, Iulian-Marius Tăiatu, Dumitru-Clementin Cercel, Iuliana Marin, Florin Pop, Florin Pop, Florin Pop

2024

Abstract

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.

Paper Citation

in Harvard Style

Gheorghiu A., Tăiatu I., Cercel D., Marin I. and Pop F. (2024). Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 549-560. DOI: 10.5220/0012466300003636

in Bibtex Style

@conference{icaart24,
author={Adrian Gheorghiu and Iulian-Marius Tăiatu and Dumitru-Clementin Cercel and Iuliana Marin and Florin Pop},
title={Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={549-560},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012466300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification
SN - 978-989-758-680-4
AU - Gheorghiu A.
AU - Tăiatu I.
AU - Cercel D.
AU - Marin I.
AU - Pop F.
PY - 2024
SP - 549
EP - 560
DO - 10.5220/0012466300003636
PB - SciTePress