An efficient approach for age-wise rice seeds classification using SURF-BOF with modified cascaded-ANFIS algorithm

Namal Rathnayake; Tuan Linh Dang; Akira Miyazaki; Yukinobu Hoshino

doi:10.1117/12.2679399

7 June 2023 An efficient approach for age-wise rice seeds classification using SURF-BOF with modified cascaded-ANFIS algorithm

Namal Rathnayake, Tuan Linh Dang, Akira Miyazaki, Yukinobu Hoshino

Author Affiliations +

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127011K (2023) https://doi.org/10.1117/12.2679399
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

It is a well-known fact that the quality of a seed highly impacts the germination of a rice seed. The age of the seed is one of the primary key points in assessing the seed quality. Therefore, this study aims to develop an AI-based machine-learning model to classify age-wise rice seeds. This study employs the SURF-BOF-based Cascaded-ANFIS algorithm for the implementation of the classifier. The proposed model performances were compared to the VGG16. Moreover, this research contributes a novel Japanese rice seed dataset to the scientific community. Furthermore, a 10-Fold cross-validation is performed to evaluate the robustness of the novel approach. The K-fold cross-validation’s mean accuracy confirmed the proposed algorithm’s higher robustness in the age-wise classification of rice seeds. Nevertheless, the results were evaluated using the confusion matrix and metrics such as precision, recall, and F1-Score. The Accuracy of Akitakomachi, Koshihikari, Yandao-8, and rice variety classification is 99%, 99%, 92%, and 97%. Analysis of the results determines the ability to classify rice by age and the general robustness of the algorithm.

Citation Download Citation

Namal Rathnayake, Tuan Linh Dang, Akira Miyazaki, and Yukinobu Hoshino "An efficient approach for age-wise rice seeds classification using SURF-BOF with modified cascaded-ANFIS algorithm", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127011K (7 June 2023); https://doi.org/10.1117/12.2679399

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Education and training

Matrices

Cross validation

Evolutionary algorithms

Image filtering

Performance modeling

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years