A systematic review of deep learning techniques for apple leaf diseases classification and detection
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 ag...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-01-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2655.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832570561618771968 |
|---|---|
| author | Assad Souleyman Doutoum Bulent Tugrul |
| author_facet | Assad Souleyman Doutoum Bulent Tugrul |
| author_sort | Assad Souleyman Doutoum |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-557ab895d7254f0a93b23493534d080d |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-557ab895d7254f0a93b23493534d080d2025-02-02T15:05:18ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e265510.7717/peerj-cs.2655A systematic review of deep learning techniques for apple leaf diseases classification and detectionAssad Souleyman Doutoum0Bulent Tugrul1Computer Science Department, University of N’djamena, N’djamena, ChadDeparment of Computer Engineering, Ankara University, Ankara, TürkiyeAgriculture 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.https://peerj.com/articles/cs-2655.pdfDeep learningAppleLeaf diseasesClassificationDetection |
| spellingShingle | Assad Souleyman Doutoum Bulent Tugrul A systematic review of deep learning techniques for apple leaf diseases classification and detection PeerJ Computer Science Deep learning Apple Leaf diseases Classification Detection |
| title | A systematic review of deep learning techniques for apple leaf diseases classification and detection |
| title_full | A systematic review of deep learning techniques for apple leaf diseases classification and detection |
| title_fullStr | A systematic review of deep learning techniques for apple leaf diseases classification and detection |
| title_full_unstemmed | A systematic review of deep learning techniques for apple leaf diseases classification and detection |
| title_short | A systematic review of deep learning techniques for apple leaf diseases classification and detection |
| title_sort | systematic review of deep learning techniques for apple leaf diseases classification and detection |
| topic | Deep learning Apple Leaf diseases Classification Detection |
| url | https://peerj.com/articles/cs-2655.pdf |
| work_keys_str_mv | AT assadsouleymandoutoum asystematicreviewofdeeplearningtechniquesforappleleafdiseasesclassificationanddetection AT bulenttugrul asystematicreviewofdeeplearningtechniquesforappleleafdiseasesclassificationanddetection AT assadsouleymandoutoum systematicreviewofdeeplearningtechniquesforappleleafdiseasesclassificationanddetection AT bulenttugrul systematicreviewofdeeplearningtechniquesforappleleafdiseasesclassificationanddetection |