Plant leaf disease detection using deep learning: a multi-dataset approach

Krishna, MS, Machado, P ORCID: https://orcid.org/0000-0003-1760-3871, Otuka, RI ORCID: https://orcid.org/0009-0006-0198-8999, Yahaya, SW ORCID: https://orcid.org/0000-0002-0394-6112, Neves dos Santos, F and Ihianle, IK ORCID: https://orcid.org/0000-0001-7445-8573, 2025. Plant leaf disease detection using deep learning: a multi-dataset approach. J, 8 (1): 4. ISSN 2571-8800

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Abstract

Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection.

Item Type:	Journal article
Publication Title:	J
Creators:	Krishna, M.S., Machado, P., Otuka, R.I., Yahaya, S.W., Neves dos Santos, F. and Ihianle, I.K.
Publisher:	MDPI
Date:	15 January 2025
Volume:	8
Number:	1
ISSN:	2571-8800
Identifiers:	Number Type 10.3390/j8010004 DOI 2345903 Other
Rights:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	04 Feb 2025 15:35
Last Modified:	04 Feb 2025 15:35
URI:	https://irep.ntu.ac.uk/id/eprint/52973

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