Classification of Rice Varieties using Convolution Neural Networks

Muhammad Bilal Shoaib Khan, Rukshanda Kamran, Mahmoud Abu Saima, Muhammad Sohail Irshad, Naila Samar Naz, Atifa Athar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350335644
DOIs
Publication statusPublished - 2023
Event2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 - Dubai, United Arab Emirates
Duration: Mar 7 2023Mar 8 2023

Publication series

Name2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

Conference

Conference2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period3/7/233/8/23

Keywords

  • Convolution Neural Networks (CNN)
  • Deep Learning

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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