An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

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.

Hyper-Parameter Tuning in Deep Neural Network Learning

“…I used the Distributed Evolutionary Algorithms in Python (DEAP) which is an evolutionary computation framework for rapid prototyping and testing of ideas [27]. It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic flow [27,28,29,30] and estimation of distribution algorithm. The benchmarks dataset MNIST and CIFAR-10 are used, which make use of categorical cross entropy as the loss function as well as the adam optimizer.…”

A Comprehensive Survey on Aquila Optimizer

“…Shankar et al [ 47 ] developed an automatic fruit classification model using hyperparameter optimized deep transfer learning. Image quality is improved using a pre-processing step called contrast enhancement.…”

Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

“…Lastly, the ECO algorithm is derived to optimally fine tune the hyperparameters [18][19][20] related to the LSTM model to enhance its detection efficiency in the cloud environment. The COA has population based metaheuristic to resolve global optimized problems [21].…”