Smart Fruit Classification using Neural Networks

Sandhiya, K.; Vidhya, M.; Saranya, M.

doi:10.31142/ijtsrd6986

Cited by 2 publications

(2 citation statements)

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“…CNN has been proven to perform well with many image classification problems. Many studies such as [19] [24] have used CNN to classify fruits. In [25] and [26] CNN was used for vegetables classification.…”

Section: • K-nearest Neighbormentioning

confidence: 99%

Date Grading using Machine Learning Techniques on a Novel Dataset

Raissouli¹,

Aljabri²,

Aljudaibi³

et al. 2020

IJACSA

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Dates grading is a crucial stage in the dates' factories. However, it is done manually in most of the Middle Eastern industries. This study, using a novel dataset, identifies the suitable machine learning techniques to grade dates based on the image of the date. The dataset consists of three different types of dates, namely, Ajwah, Mabroom, and Sukkary with each having three different grades. The dates were obtained from Manafez company and graded by their experts. The color, size and texture of the dates are the features that have been considered in this work. To determine the color, we have used color properties in RGB (red, green, and blue) color space. For measuring the size, we applied the best least-square fitting ellipse. To analyze the texture, we used Weber local descriptor to distinguish between texture patterns. In order to identify the suitable grading classifier, we have experimented three approaches, namely, k-nearest neighbor (KNN), support vector machine (SVM) and convolutional neural network (CNN). Our experiments have shown that CNN is the best classifier with an accuracy of 98% for Ajwah, 99% for Mabroom, and 99% for Sukkary. Hence, the CNN classifier has been incorporated in our date grading system.

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Section: • K-nearest Neighbormentioning

confidence: 99%

Date Grading using Machine Learning Techniques on a Novel Dataset

Raissouli¹,

Aljabri²,

Aljudaibi³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…Some researchers have used backpropagation neural networks on di®erent datasets and to train the networks for classi¯cation. [2][3][4][5][6][7][8][9][10][11] They have used default values of all parameters like learning rate, momentum, maximum epochs, etc. Some researchers have applied di®erent training algorithms in di®erent prediction problems.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India

Mandal

Ghosh

Shit

et al. 2020

Vietnam J. Comp. Sci.

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Various training algorithms are used in artificial neural networks for updating the weights during training the network. But, the selection of the appropriate training algorithm is dependent on the input–output mapping of dataset for which the network is constructed. In this paper, a framework has been proposed consisting of five modules to select the optimal training algorithm for predicting pig breeds from their images. The individual pig images from five pig-breeds have been captured using inbuilt camera of mobile phone and the contour of pig has been segmented from each captured image by HUE-based segmentation algorithm. In Statistical Parameter and Color Component retrieval module, parameters like entropy, standard deviation, variance, mean, median, and mode and color properties like hue, saturation, value (HSV) extracted from the content of each segmented image. Values of all extracted parameters have been transferred into Training Algorithm Selection Module. In this module, a fitting neural network with different numbers of hidden neurons has been executed by feeding all extracted values from pig images for mapping their breeds. Ten training algorithms have been applied on the same extracted dataset separately for five epochs each keeping other network parameters constants. The mean square error (MSE) and correlation coefficient ([Formula: see text]) for the validation set have been calculated after adjustment of weights and biases in each connection of the neurons. One training algorithm among 10 and its suitable number of hidden neurons has been selected based on comparative analysis for getting lower MSE and higher [Formula: see text] in the validation set. Then, the fitting network with selected training algorithm has been run on the same extracted datasets until the stopping condition is reached. Then the test set images are fed into the network and the network output has been categorized to class which has been assigned to each breed of pig in Breed Prediction Module. The proposed framework has been able to predict breeds with 96.00% accuracy, achieved by the trial with 50 images of the test set. It may be concluded that the Neuro Statistic Neural Network model may be used for breed prediction of pigs by using images of individual pigs.

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Smart Fruit Classification using Neural Networks

Cited by 2 publications

References 0 publications

Date Grading using Machine Learning Techniques on a Novel Dataset

Date Grading using Machine Learning Techniques on a Novel Dataset

A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India

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