Improved AlexNet with Inception-V4 for Plant Disease Diagnosis.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
12
05
2022
revised:
19
07
2022
accepted:
21
07
2022
entrez:
20
9
2022
pubmed:
21
9
2022
medline:
23
9
2022
Statut:
epublish
Résumé
Timely disease detection and pest treatment are key issues in modern agricultural production, especially in large-scale crop agriculture. However, it is very time and effort-consuming to identify plant diseases manually. This paper proposes a deep learning model for agricultural crop disease identification based on AlexNet and Inception-V4. AlexNet and Inception-V4 are combined and modified to achieve an efficient but good performance. Experimental results on the expanded PlantVillage dataset show that the proposed model outperforms the compared methods: AlexNet, VGG11, Zenit, and VGG16, in terms of accuracy and
Identifiants
pubmed: 36124118
doi: 10.1155/2022/5862600
pmc: PMC9482484
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5862600Informations de copyright
Copyright © 2022 Zhuoxin Li et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
Références
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