Fruit Classification using Statistical Features in SVM Classifier

Kumari, R. Shantha Selva; Gomathy, V.

doi:10.1109/icees.2018.8442331

Cited by 24 publications

(12 citation statements)

References 8 publications

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“…The result presented by Alzubi et al [6] uses binary SVM for classifying the date fruit into two categories whether it is eatable or non-eatable. Another study conducted by [14] with two feature color and texture resulted in lower accuracy than that of proposed work with feature fusion (color, shape and texture feature), using multiclass approach. The training set features improves the accuracy of the model therefore high accuracy is achieved when appropriate feature selection for multiclass classification problem.…”

Section: Discussionmentioning

confidence: 69%

Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

Fatima¹,

Seshashayee²

2020

IJITEE

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The fruit categorization according to their visual quality has recently experienced tremendous growth in the field of agriculture and food products. Due to post-harvest loses during handling and processing, there is an increasing demand for quality products in agro industry which requires accuracy to predict the fruit. Various techniques of machine learning have been successfully applied for classifying the fruit built on binary class. In this paper, machine leaning technique is used to automate the process of categorization and to improve the accuracy of different types of fruits by feature selection. To categorized images domain specific features such as color, shape and textual features are considered. Statistical color features are extracted from the image, bounding box feature for shape features and gray-level co-occurrence matrix (GLCM) is used to extract the textual feature of an image. These features are combined in a single feature fusion. A support vector machine (SVM) classification model is trained using training set features on fruit360 dataset which includes six fruit categories (classes) with two sub category (sub-classes) which builds multiclass classification task. We present one-vs-one coding design of Error correcting output codes (ECOC) and apply to SVM classifier; validation followed a fivefold cross validation strategy. The result shows that the textual features combined with color and shape feature improved fruit classification accuracy.

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Section: Discussionmentioning

confidence: 69%

Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

Fatima¹,

Seshashayee²

2020

IJITEE

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“…Fruit pictures are classified using an SVM classifier. Overall, the suggested methods have a classification accuracy of 95.3% [22].…”

Section: Related Workmentioning

confidence: 95%

Shallots Classification using CNN

Wathan

2022

ijicom

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Shallots are an essential plant for the commercial and home industries included in the Allium genus. Choosing the type of onion based on the characteristics is very easy for humans to do but not easy for humans to do in the spice industry; therefore, machines will replace human limitations in recognizing the type of shallots in the spice industry. Inspired by the success of research on the classification of shallots using SVM, we propose CNN to tackle the problem of classifying types of shallots based on shape and texture features. This study uses the CNN method's performance to categorize different varieties of shallots based on their shape and texture features. In a shallot classification test, our approach promises higher accuracy and lower loss than standard machine techniques.

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“…The structure's last portion performs an estimation of the classes of fruits. Kumari and Gomathy [15] recommend a classical method that utilizes texture features and color for fruit classification. The conventional fruit classification technique is reliable upon manual function on the basis of visual ability.…”

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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Fruit Classification using Statistical Features in SVM Classifier

Cited by 24 publications

References 8 publications

Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

Healthy Fruits Image Label Categorization through Color Shape and Texture Features Based on Machine Learning Algorithm

Shallots Classification using CNN

An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification

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