2022
DOI: 10.1002/cpe.7523
|View full text |Cite
|
Sign up to set email alerts
|

Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

Abstract: Summary The earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization‐based deep learning (SSSO‐based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 31 publications
(73 reference statements)
0
0
0
Order By: Relevance
“…This strategy contributes to prompt and efficient disease management by allowing farmers and agricultural professionals to use their cellphones or tablets for disease identification, resulting in increased crop health and output. The research in [6] advances rice leaf disease analysis by providing a novel methodology that combines SSSO with deep learning. This approach delivers useful insights for farmers and agricultural professionals by correctly categorising illnesses and assessing their severity, allowing them to make informed decisions about disease management and crop protection.…”
Section: Literature Reviewmentioning
confidence: 99%
“…This strategy contributes to prompt and efficient disease management by allowing farmers and agricultural professionals to use their cellphones or tablets for disease identification, resulting in increased crop health and output. The research in [6] advances rice leaf disease analysis by providing a novel methodology that combines SSSO with deep learning. This approach delivers useful insights for farmers and agricultural professionals by correctly categorising illnesses and assessing their severity, allowing them to make informed decisions about disease management and crop protection.…”
Section: Literature Reviewmentioning
confidence: 99%