A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

Xue, Gang; Liu, Shifeng; Ma, Yicao

doi:10.1007/s40747-020-00192-x

Cited by 46 publications

(26 citation statements)

References 45 publications

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“…is model was proposed by Xue et al [109] for hybrid deep learning-based fruit classification. is model pretrained images with a convolution autoencoder and extracted image features using an attention-based DenseNet.…”

Section: Convolutional Autoencoder-attention-based Densenet (Cae-adn)mentioning

confidence: 99%

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma

Qin

Heyat

et al. 2022

Mathematical Problems in Engineering

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Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

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Section: Convolutional Autoencoder-attention-based Densenet (Cae-adn)mentioning

confidence: 99%

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma

Qin

Heyat

et al. 2022

Mathematical Problems in Engineering

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“…In [14], the researchers advanced a hybrid DL-related fruit image classification structure called attention-related densely connected convolution network with convolution auto-encoder (CAE-ADN), that employs a CAE for pretraining the images and leverages an attention-related DenseNet for extracting the image features. In the opening portion of the structure, an unsupervised technique with a group of images is applied to pretrain the greedy layer-wised CAE.…”

Section: Related Workmentioning

confidence: 99%

An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

et al. 2022

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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.

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“…In a hybrid deep learning approach for fruit classification, first, hand crafted features are extracted. In the next step, it uses convolution autoencoder to pre-train the images and then applies an attention based DenseNet to process the images [17]. A tomato disease detection and classification approach uses transfer learning [6,18].…”

Section: Related Workmentioning

confidence: 99%

Fruits and Vegetables Freshness Categorization Using Deep Learning

Fahad¹,

Tahir²,

Rasheed³

et al. 2022

Computers, Materials &Amp; Continua

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The nutritional value of perishable food items, such as fruits and vegetables, depends on their freshness levels. The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only. We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories: purefresh, medium-fresh, and rotten. We gathered a dataset comprising of 60K images of 11 fruits and vegetables, each is further divided into three categories of freshness, using hand-held cameras. The recognition and categorization of fruits and vegetables are performed through two deep learning models: Visual Geometry Group (VGG-16) and You Only Look Once (YOLO), and their results are compared. VGG-16 classifies fruits and vegetables and categorizes their freshness, while YOLO also localizes them within the image. Furthermore, we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree. A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset. The dataset is publicly available for further evaluation by the research community.

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A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

Cited by 46 publications

References 45 publications

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

Fruits and Vegetables Freshness Categorization Using Deep Learning

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