A deep learning-based approach for the detection of cucumber diseases
Publication date
2025-04-11
Document type
Forschungsartikel
Author
Organisational unit
Publisher
PLOS
Series or journal
PLOS ONE
ISSN
Periodical volume
20
Periodical issue
4
Article ID
e0320764
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
Cucumbers play a significant role as a greenhouse crop globally. In numerous countries, they are fundamental to dietary practices, contributing significantly to the nutritional patterns of various populations. Due to unfavorable environmental conditions, they are highly vulnerable to various diseases. Therefore the accurate detection of cucumber diseases is essential for maintaining crop quality and ensuring food security. Traditional methods, reliant on human inspection, are prone to errors, especially in the early stages of disease progression. Based on a VGG19 architecture, this paper uses an innovative transfer learning approach for detecting and classifying cucumber diseases, showing the applicability of artificial intelligence in this area. The model effectively distinguishes between healthy and diseased cucumber images, including Anthracnose, Bacterial Wilt, Belly Rot, Downy Mildew, Fresh Cucumber, Fresh Leaf, Pythium Fruit Rot, and Gummy Stem Blight. Using this novel approach, a balanced accuracy of 97.66% on unseen test data is achieved, compared to a balanced accuracy of 93.87% obtained with the conventional transfer learning approach, where fine-tuning is employed. This result sets a new benchmark within the dataset, highlighting the potential of deep learning techniques in agricultural disease detection. By enabling early disease diagnosis and informed agricultural management, this research contributes to enhancing crop productivity and sustainability.
Description
This is an open access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
Cite as
Raufer, L.; Wiedey, J.; Müller, M.; Penava, P.; Buettner, R.: A deep learning-based approach for the detection of cucumber diseases. PLOS ONE 20(4):e0320764, 2025.
Version
Published version
Access right on openHSU
Metadata only access
Open Access Funding
HSU publication fund