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Cited by 25

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A systematic review of deep learning techniques for apple leaf diseases classification and detection

Doutoum, Assad Souleyman, Tugrul, Bulent·2025·PeerJ Computer Science
DOI 10.7717/peerj-cs.2655
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Abstract

Agriculture sustains populations and provides livelihoods, contributing to socioeconomic growth. Apples are one of the most popular fruits and contains various antioxidants that reduce the risk of chronic diseases. Additionally, they are low in calories, making them a healthy snack option for all ages. However, several factors can adversely affect apple production. These issues include diseases that drastically lower yield and quality and cause farmers to lose millions of dollars. To minimize yield loss and economic effects, it is essential to diagnose apple leaf diseases accurately and promptly. This allows targeted pesticide and insecticide use. However, farmers find it difficult to distinguish between different apple leaf diseases since their symptoms are quite similar. Computer vision applications have become an effective tool in recent years for handling these issues. They can provide accurate disease detection and classification through massive image datasets. This research analyzes and evaluates datasets, deep learning methods and frameworks built for apple leaf disease detection and classification. A systematic analysis of 45 articles published between 2016 and 2024 was conducted to evaluate the latest developments, approaches, and research needs in this area.

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Citations

Cites

Research works printed but not linked: 53

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Metadata

Authors
Doutoum, A. S., & Tugrul, B.
Venue
PeerJ Computer Science
Publisher
PeerJ
Published
2025-01-31
Volume
11
Pages
e2655
ISSN
2376-5992
DOI
10.7717/peerj-cs.2655
Cited by
25
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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