An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

M T Vasumathi; M Kamarasan

doi:10.17485/IJST/v14i16.432

Article

An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i16.432

Year: 2021, Volume: 14, Issue: 16, Pages: 1310-1319

Original Article

An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

M T Vasumathi^1*, M Kamarasan²

¹Research Scholar, Department of Computer & Information Science, Annamalai University,
Annamalai Nagar, Chidambaram, Tamilnadu, India
²Assistant Professor, Department of Computer & Information Science, Annamalai University,
Annamalai Nagar, Chidambaram, Tamilnadu, India

*Corresponding Author
Email: [email protected]

Received Date:12 March 2021, Accepted Date:23 April 2021, Published Date:10 May 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To employ a deep learning technique that would sort the fruits into normal and abnormal based on the features such as fruit colour, number of fruit spots, and shape of the fruit. Methods: A combined CNN LSTM deep learning model is applied to classify a set of 6519 fruits into two classes namely normal and abnormal. The dataset is an excel file with each record consisting of 9 features. Convolutional Neural Networks (CNN) are applied for deep feature extraction and Long-Short Term Memory (LSTM) is used to detect the class based on extracted features. Findings: The proposed system achieved an accuracy of 98.17%, specificity of 98.65%, sensitivity of 97.77%, and an F1-score of 98.39%. Novelty: The sensitivity of disease detection was less with lesser availability of enhanced detection methods for detecting disease in earlier stages. The issue with these various existing algorithms is that the accuracy was reduced since some sources of errors were not eliminated. Deep Learning delivers methodologies, approaches, and functionalities that can help to resolve analytic and predictive analysis accurately.

Keywords: Deep Learning; CNN LSTM; Classification; Hyperparameters

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Copyright

© 2021 Vasumathi & Kamarasan.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)