Hybrid classifier model for fruit classification

Gill, Harmandeep Singh; Khehra, Baljit Singh

doi:10.1007/s11042-021-10772-9

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…These input units are the RGB histogram, the RGB centroid acquired from K-means clustering, and single RGB color. In [13], the researchers suggested a new fruit classification method that uses Long Short-Term Memory (LSTM), RNN structures, and CNN features. Type-II fuzzy advancement was further utilized as a preprocessing device for advancing the images.…”

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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Section: Related Workmentioning

confidence: 99%

An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

et al. 2022

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“…The usage of image processing has been wide increasingly in agricultural field to automate its processes; automation system can be implemented in crop ripeness monitoring, crop disease detection, fruits and vegetables recognition (Al-falluji, 2016). The automatic fruit's image classification has attract wide attention by researchers worldwide cause its offers numerous solutions such as reducing manual effort to a large extent as well as time evolvement (Gill & Khehra, 2021). Fruits recognition systems can be utilized in many real-life implementations, such in store checkout, where it may be utilize rather than manual scanner tags; moreover, for helping eye weakness people as a supportive appliances, an educational tool for small children and Down syndrome patients.…”

Section: Introductionmentioning

confidence: 99%

A Recognition and Classification of Fruit Images Using Texture Feature Extraction and Machine Learning Algorithms

Behnam Israel,

Ismail Al-Sulaifanie,

Khorsheed Al-Sulaifanie

2024

ACAD J NAWROZ UNIV

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Fruits classification is demanded in some fields, such as industrial agriculture. Automatic fruit classification from their digital image plays a vital role in those fields. The classification encounters several challenges due to capturing fruits’ images from different viewing angle, rotation, and illumination pose. In this paper a framework for recognition and classification of fruits from their images have been proposed depending on texture features, the proposed system rely on three phases; firstly, pre-processing, as images need to be resized, filtered, color convert, and threshold in order to create a fruit mask which is used for fruit’s region of interest segmentation; followed by two methods for texture features extraction, first method utilize Local Binary Pattern (LBP), while the second method uses Principal Component Analysis (PCA) to generate features vector for each fruit image. Classification is the last phase; two supervised machine learning algorithms; K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are utilized to identity and recognize the fruits images classes. Both methods are tested using 1200 fruits images, from 12 classes acquired from Fruits-360 database. The results show that combining LBP with K-NN, and SVM yields the best accuracy up to 100% and 89.44% respectively, while the accuracy of applying PCA with K-NN and SVM reached to 86.38 % and 85.83% respectively.

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“…Peak Signal to Noise Ratio PSNR is defined as [23]: Where, the root-mean-squared error, or RMSE, is defined as:…”

mentioning

confidence: 99%

Fruit Image Segmentation Using Teacher-Learner Optimization Algorithm and Fuzzy Entropy

Gill

Kumar²,

Chakrabarti

2022

ECS Trans.

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To perform robust fruit image segmentation is a challenging task due to several variances in the varieties. Thresholding is one of the popular segmentation methods in recent times. The fuzzy entropy scheme has been mostly applied to image thresholding. Fuzzy membership functions are the major source for segmenting the image by fuzzy entropy and thresholding. In the proposed paper, the Teacher-Learner-Based Optimization (TLBO) algorithm is involved in searching for optimal threshold values combination for fruit image segmentation based on fuzzy entropy. The proposed scheme is performed on red apple, green apple, golden apple, guava, and orange fruit images. For comparison of segmentation results of the proposed scheme with existing approaches, PSNR and uniformity parameters are utilized. It has been observed that the performance of the proposed approach is more effective than GA (Genetic Algorithm), BFO (Bacteria Foraging Optimization), and HBMO (Honey Bee Mating Optimization) approaches.

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Hybrid classifier model for fruit classification

Cited by 16 publications

References 31 publications

An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

A Recognition and Classification of Fruit Images Using Texture Feature Extraction and Machine Learning Algorithms

Fruit Image Segmentation Using Teacher-Learner Optimization Algorithm and Fuzzy Entropy

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