Potato Leaf Disease Detection Approach Based on Transfer Learning with Spatial Attention

Rima Grati, Emna Abdallah, Khouloud Boukadi, Ahmed Smaoui

2024

Abstract

Agricultural productivity is vital to global economic development and growth. When crops are affected by diseases, it adversely impacts a nation’s financial resources and agricultural output. Early detection of crop diseases can minimize losses for farmers and enhance production. Symptoms of diseases may take form in different parts of plants. However, the leaves, especially those of potatoes, are most commonly used in disease detection because they are buried deep in the ground. Deep learning-based CNN methods have become the standard for addressing most technical image identification and classification challenges. To improve training performance, the attention mechanism in deep learning helps the model concentrate on the informative data segments and extract the discriminative properties of inputs. This paper investigates spatial attention, which aims to highlight important local regions and extract more discriminative features. Moreover, the most popular CNN architectures, MobileNetV2, DenseNet121, and InceptionV3, were applied to transfer learning for potato disease classification and then fine-tuned by the publicly available dataset of PlantVillage. The experiments reveal that the proposed Att-MobileNetV2 model performs better than other state-of-the-art methods. It achieves an identification F-measure of 98% on the test dataset, including images from Google. Finally, we utilized Grad-CAM++ in conjunction with the Att-MobileNetV2 method to provide an interpretable explanation of the model’s performance. This approach is particularly effective in localizing the predicted areas, clarifying how CNN-based models identify the disease, and ultimately helping farmers trust the model’s predictions.

Paper Citation

in Harvard Style

Grati R., Abdallah E., Boukadi K. and Smaoui A. (2024). Potato Leaf Disease Detection Approach Based on Transfer Learning with Spatial Attention. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 146-155. DOI: 10.5220/0013066200003822

in Bibtex Style

@conference{icinco24,
author={Rima Grati and Emna Abdallah and Khouloud Boukadi and Ahmed Smaoui},
title={Potato Leaf Disease Detection Approach Based on Transfer Learning with Spatial Attention},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={146-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013066200003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Potato Leaf Disease Detection Approach Based on Transfer Learning with Spatial Attention
SN - 978-989-758-717-7
AU - Grati R.
AU - Abdallah E.
AU - Boukadi K.
AU - Smaoui A.
PY - 2024
SP - 146
EP - 155
DO - 10.5220/0013066200003822
PB - SciTePress