Plant Disease Identification Based on Encoder–Decoder Model

Plant disease identification is a crucial issue in agriculture, and with the advancement of deep learning techniques, early and accurate identification of plant diseases has become increasingly critical. In recent years, the rise of vision transformers has attracted significant attention from resear...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Agronomy (Basel) Jg. 14; H. 10; S. 2208
Hauptverfasser: Feng, Wenfeng, Sun, Guoying, Zhang, Xin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2024
Schlagworte:
Accuracy
Agriculture
Classification
Coders
Competitiveness
Computational linguistics
Computer vision
Crop diseases
Crops
Datasets
Deep learning
Diseases
Identification
imagenet
Language processing
Leaf-curl
Leafspot
Leaves
Machine learning
Natural language interfaces
Neural networks
Plant diseases
Potatoes
Precision agriculture
Rust fungi
Soybeans
Tomatoes
Transfer learning
Viral diseases
vision transformer
ISSN:2073-4395, 2073-4395
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Plant disease identification is a crucial issue in agriculture, and with the advancement of deep learning techniques, early and accurate identification of plant diseases has become increasingly critical. In recent years, the rise of vision transformers has attracted significant attention from researchers in various vision-based application areas. We designed a model with an encoder–decoder architecture to efficiently classify plant diseases using a transfer learning approach, which effectively recognizes a large number of plant diseases in multiple crops. The model was tested on the “PlantVillage”, “FGVC8”, and “EMBRAPA” datasets, which contain leaf information from crops such as apples, soybeans, tomatoes, and potatoes. These datasets cover diseases caused by fungi, including rust, spot, and scab, as well as viral diseases such as leaf curl. The model’s performance was rigorously evaluated on datasets, and the results demonstrated its high accuracy. The model achieved 99.9% accuracy on the “PlantVillage” dataset, 97.4% on the “EMBRAPA” dataset, and 91.5% on the “FGVC8” dataset, showcasing its competitiveness with other state-of-the-art models. This study provides a robust and reliable solution for plant disease classification and contributes to the advancement of precision agriculture.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy14102208