LIDL4Oliv: A Lightweight Incremental Deep Learning Model for Classifying Olive Diseases in Images

Emna Guermazi, Emna Guermazi, Emna Guermazi, Afef Mdhaffar, Afef Mdhaffar, Mohamed Jmaiel, Mohamed Jmaiel, Bernd Freisleben

2024

Abstract

We present LIDL4Oliv, a novel lightweight incremental deep learning model for classifying olive diseases in images. LIDL4Oliv is first trained on a novel annotated dataset of images with complex background. Then, it learns from a large-scale deep learning model, following a knowledge distillation approach. Finally, LIDL4Oliv is successfully deployed as a cross-platform application on resource-limited mobile devices, such as smartphones. The deployed deep learning can detect olive leaves in images and classify their states as healthy or unhealthy, i.e., affected by one of the two diseases “Aculus Olearius” and “Peacock Spot”. Our mobile application supports the collection of real data during operation, i.e., the training dataset is continuously augmented by newly collected images of olive leaves. Furthermore, our deep learning model is retrained in a continuous manner, whenever a new set of data is collected. LIDL4Oliv follows an incremental update process. It does not ignore the knowledge of the previously deployed model, but it (1) incorporates the current weights of the deployed model and (2) employs fine-tuning and knowledge distillation to create an enhanced incremental lightweight deep learning model. Our conducted experiments show the impact of using our complex background dataset to improve the classification results. They demonstrate the effect of using knowledge distillation in enhancing the performance of the deployed model on resource-limited devices.

Paper Citation

in Harvard Style

Guermazi E., Mdhaffar A., Jmaiel M. and Freisleben B. (2024). LIDL4Oliv: A Lightweight Incremental Deep Learning Model for Classifying Olive Diseases in Images. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 583-594. DOI: 10.5220/0012466900003636

in Bibtex Style

@conference{icaart24,
author={Emna Guermazi and Afef Mdhaffar and Mohamed Jmaiel and Bernd Freisleben},
title={LIDL4Oliv: A Lightweight Incremental Deep Learning Model for Classifying Olive Diseases in Images},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={583-594},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012466900003636},
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 - LIDL4Oliv: A Lightweight Incremental Deep Learning Model for Classifying Olive Diseases in Images
SN - 978-989-758-680-4
AU - Guermazi E.
AU - Mdhaffar A.
AU - Jmaiel M.
AU - Freisleben B.
PY - 2024
SP - 583
EP - 594
DO - 10.5220/0012466900003636
PB - SciTePress