Dataset for localization and classification of Medjool dates in digital images

Pérez-Pérez, Dalila Blanca; Salomón-Torres, Ricardo; García-Vázquez, Juan Pablo

doi:10.1016/j.dib.2021.107116

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Additionally, researchers will perform experiments and testing along with other date fruit varieties. It can enhance the classification of different varieties of date fruits, like the work published by other researchers [7 , 8] .…”

Section: Value Of the Datamentioning

confidence: 96%

A novel dataset of date fruit for inspection and classification

Maitlo,

Shaikh,

Arain

2024

Data in Brief

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Section: Value Of the Datamentioning

confidence: 96%

A novel dataset of date fruit for inspection and classification

Maitlo,

Shaikh,

Arain

2024

Data in Brief

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“…This system is composed of a conveying unit, illumination and capturing unit, and sorting unit, where each unit is designed to perform a particular role in the grading process. To handle the problem of labeled data scarcity, a dataset of different sizes and maturity stages of Medjool dates was introduced in [23].…”

Section: Related Workmentioning

confidence: 99%

Date Fruit Sorting Based on Deep Learning and Discriminant Correlation Analysis

et al. 2022

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Date fruit is among the major crops in the middle-east region, where millions of tons are harvested every year. Date is a healthy fruit, which involves sugars, minerals and vitamins. In addition, it helps preventing human body from several diseases such as cancer and heart diseases. Date sorting is a fundamental step in the date industry. However, manually conducting such an operation, by human labors, is expensive and time-consuming. In this paper, we propose a method for classifying the type of date fruit by incorporating supervised and unsupervised deep networks. Specifically, we use discriminant correlation analysis (DCA) algorithm to fuse features learned from convolution neural networks (VGG-F) and an unsupervised network called PCANet. DCA jointly performs feature fusion and dimensionality reduction with a low computational complexity. To carry out experiments, we introduce a new benchmark dataset of date fruit images from 20 date varieties. Our benchmark is, to the best of our knowledge, the largest one in terms of number of varieties. Note that the dataset is publicly available at https://unsat.000webhostapp.com/dataset . Experimental results demonstrate the utility of DCA as well as the complementarity of the fused features. It has also been shown the effectiveness of the proposed method compared to several relevant methods.

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“…These approaches may be found here. [20][21] Two of them are statistically-based learning approaches for feature extraction that are combined with a standard artificial neural network (ANN) in order to function as a classifier and detector. The first approach employs four different forms of predictive learning derived from descriptive statistical measurements, such as the mean, median, variance, and standard deviation.…”

Section: Literature Surveymentioning

confidence: 99%

Construction of binary classification model by ML with IP on the CIFAR10 dataset

Madhanmohan¹,

E.²

2022

INDJCSE

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Learning through machines is currently considered to be one of the most popular topics on a global scale. It is even possible to say this about the modern electricity that is used in the world today. To be more specific about what machine learning is, however, we can say that it is just one method of teaching the machine by providing it with a significant amount of data. Machine Learning algorithms allow you to do image processing at scale, and with great detail. This work finds that both the ASC and the J48 have a high level, in addition to the same level of accuracy, which is 90%; both the ASC and the J48 have a high level, in addition to the same level of precision, which is 0.92. The ASC and J48 both have a high level of performance as well as the same recall level, which is 0.90; the ASC and J48 both have a high level of performance as well as the same ROC level, which is 0.98; and the ASC and J48 both have a high level of performance as well as the same PRC level, which is 0.97. The ASC and J48 both have the same high F-Measure value, which is 0.90; the ASC and J48 both have the same high Kappa value, which is 0.80; the ASC and J48 both have the same high MCC value, which is 0.82; and the ASC and J48 both have the same best performance, which is 0.10 of MAE. Both the ASC and the J48 have achieved the same level of performance, which is RMSE of 0.29. Both the ASC and the J48 have achieved the same best performance, which is equal to 20% of RAE. Both the ASC and the J48 have achieved the same level of performance, which is 57.27% of RRSE. Low-level accuracy, which is 60%, is possessed by the IBK. The precision of the FLDA is only at a low level, which is 0.65. Low level recall, which is 0.60, is exhibited by the IBK. The PRC for the IBK is quite low, coming in at 0.60. IBK has a PRC that is relatively low, coming in at 0.57. The F-Measure for the FLDA is the lowest possible, coming in at 0.55. The Kappa level of the IBK is quite low, coming in at 0.20. The MCC level in the IBK is the lowest possible, coming in at 0.25. The worst performance level for the IBK is 0.41 of MAE; the worst performance level for the IBK is 0.60 of RMSE; the worst performance level for the IBK is 82% of RAE; and the worst performance level for the IBK is 120.42% of RRSE.

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Dataset for localization and classification of Medjool dates in digital images

Cited by 8 publications

References 4 publications

A novel dataset of date fruit for inspection and classification

A novel dataset of date fruit for inspection and classification

Date Fruit Sorting Based on Deep Learning and Discriminant Correlation Analysis

Construction of binary classification model by ML with IP on the CIFAR10 dataset

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