An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

Kathiresan Shankar, Sachin Kumar, Ashit Kumar Dutta, Ahmed Alkhayyat, Anwar Ja’afar Mohamad Jawad, Ali Hashim Abbas and Yousif K. Yousif
Additional contact information
Kathiresan Shankar: Big Data and Machine Learning Lab, South Ural State University, 454080 Chelyabinsk, Russia
Sachin Kumar: Big Data and Machine Learning Lab, South Ural State University, 454080 Chelyabinsk, Russia
Ashit Kumar Dutta: Department of Computer Science and Information System, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia
Ahmed Alkhayyat: College of Technical Engineering, The Islamic University, Najaf 61001, Iraq
Anwar Ja’afar Mohamad Jawad: Department of Computer Techniques Engineering, Al-Rafidain University College, Baghdad 10064, Iraq
Ali Hashim Abbas: College of Information Technology, Imam Ja’afar Al-Sadiq University, Al-Muthanna 66002, Iraq
Yousif K. Yousif: Department of Computer Technical Engineering, Al-Hadba University College, Mosul 41001, Iraq

Mathematics, 2022, vol. 10, issue 13, 1-18

Abstract: Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. As a result, intelligent systems using machine learning and computer vision approaches were explored for ripeness grading, fruit defect categorization, and identification over the last few years. Recently, deep learning (DL) methods for classifying fruits led to promising performance that effectively extracts the feature and carries out an end-to-end image classification. This paper introduces an Automated Fruit Classification using Hyperparameter Optimized Deep Transfer Learning (AFC-HPODTL) model. The presented AFC-HPODTL model employs contrast enhancement as a pre-processing step which helps to enhance the quality of images. For feature extraction, the Adam optimizer with deep transfer learning-based DenseNet169 model is used in which the Adam optimizer fine-tunes the initial values of the DenseNet169 model. Moreover, a recurrent neural network (RNN) model is utilized for the identification and classification of fruits. At last, the Aquila optimization algorithm (AOA) is exploited for optimal hyperparameter tuning of the RNN model in such a way that the classification performance gets improved. The design of Adam optimizer and AOA-based hyperparameter optimizers for DenseNet and RNN models show the novelty of the work. The performance validation of the presented AFC-HPODTL model is carried out utilizing a benchmark dataset and the outcomes report the promising performance over its recent state-of-the-art approaches.

Keywords: fruit classification; deep transfer learning; recurrent neural network; Adam optimizer; hyperparameter tuning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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