2023
DOI: 10.21203/rs.3.rs-2906389/v1
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Design of Accurate Multi-class Optimized Lightweight Convolution Neural Network for Rice Varieties Classification

Abstract: Worldwide, more than 40k rice varieties are existing, each with different nutritional content and quality. Identifying these has to be consistent, automated, and accurate. Considering the feature extraction process, convolution Neural Networks (CNN) are preferred over machine learning (ML) for this classification. Transfer learning approaches help to optimize the CNN model; therefore, it fits in an FPGA. Seven different CNN models were proposed to classify five rice varieties, each model differs based on the: … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 14 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?