TY - GEN
T1 - Classification of Rice Varieties using Convolution Neural Networks
AU - Shoaib Khan, Muhammad Bilal
AU - Kamran, Rukshanda
AU - Saima, Mahmoud Abu
AU - Sohail Irshad, Muhammad
AU - Naz, Naila Samar
AU - Athar, Atifa
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The classification of rice varieties is a crucial task in the agricultural industry, as it helps farmers to identify and manage crops effectively. In recent years, deep learning algorithms have shown promising results in image recognition tasks, and have been applied to agricultural applications to classify crop varieties. This study proposes a deep learning-based CNN approach to classify five variants of rice varieties based on their images. The proposed method utilizes convolutional neural networks (CNNs) to learn the features from rice images and classify them into their respective categories. The dataset used in this study consists of 300 images of five different rice varieties, which are collected from various sources and angles. The experimental results demonstrate that the proposed method achieves high accuracy in classifying the five rice varieties. The accuracy of the classification algorithm is evaluated using different metrics as mentioned in the literature.
AB - The classification of rice varieties is a crucial task in the agricultural industry, as it helps farmers to identify and manage crops effectively. In recent years, deep learning algorithms have shown promising results in image recognition tasks, and have been applied to agricultural applications to classify crop varieties. This study proposes a deep learning-based CNN approach to classify five variants of rice varieties based on their images. The proposed method utilizes convolutional neural networks (CNNs) to learn the features from rice images and classify them into their respective categories. The dataset used in this study consists of 300 images of five different rice varieties, which are collected from various sources and angles. The experimental results demonstrate that the proposed method achieves high accuracy in classifying the five rice varieties. The accuracy of the classification algorithm is evaluated using different metrics as mentioned in the literature.
KW - Convolution Neural Networks (CNN)
KW - Deep Learning
UR - http://www.scopus.com/inward/record.url?scp=85160751950&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85160751950&partnerID=8YFLogxK
U2 - 10.1109/ICBATS57792.2023.10111346
DO - 10.1109/ICBATS57792.2023.10111346
M3 - Conference contribution
AN - SCOPUS:85160751950
T3 - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
BT - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
Y2 - 7 March 2023 through 8 March 2023
ER -