HW/SW Co-‘esign for Dates Classification on Xilinx Zynq SoC

Ammari, Ahmed Chiheb; Khriji, Lazhar; Awadalla, Medhat

doi:10.23919/fruct48808.2020.9087548

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…The best result was obtained by a back propagation neural network, then SVM, and then KNN classifiers in terms of the accuracy of mean scores [19]. Ammari et al [11] studied the evaluation of Khalas, Khenaizi, Fardh, Qash, Naghal, and Maan dates using dedicated computer vision. The pre-processing and segmentation of date images were the first steps in the system.…”

Section: Related Workmentioning

confidence: 99%

“…The F1-score in Equation 11Combines the precision and recall results in data classification that provide overall prediction performance. F1-score = (2× (Precision×Recall)) / (Precision+Recall) (11) The Receiver Operator Characteristics (ROC) Curve is also operated to determine the performance of the model. In the ROC curve, specificity is exposed on the x-axis and sensitivity is shown on the y-axis.…”

Section: ) Precisionmentioning

confidence: 99%

“…The development of image classification through AI systems has good durability at a low cost, as well as offering high precision and being computationally fast to evaluate fruits in adverse weather and typical environmental conditions [9]. As a result, industries have adopted computer vision techniques to classify grades and sort dates depending on several features such as color [7][8][9][10], texture [7], and size [11], which were previously managed manually [1].…”

Section: Introductionmentioning

confidence: 99%

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Innovative Date Fruit Classifier Based on Scatter Wavelet and Stacking Ensemble

Al-Kharaz,

Alwahhab,

Sabeeh

2024

HighTech. Innov. J.

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Dates are essential fruits loaded with vital nutrients that keep bones healthy and prevent bone-related disorders. Approximately 8.46 million tons of different types of dates are cultivated and produced annually around the globe. There are more than 400 types of dates that are time-consuming and expensive to produce. Classifying them using conventional methods is labor-intensive, and this is one of the biggest problems for the date industry. Dataset fruit classification plays a vital role in the food industry. Dates can be classified from a luxury class to a less quality class. Accordingly, the food industry needs an automotive date fruit classifier that can work in food factories. This study proposes a pioneering method to classify date fruit that relies on extracting features from the texture of dates using Scattering Wavelet Transformation (SWT). The SWT yields in numeric coefficients were found to be immune to the deformation of invariants. This feature set trains an ensemble classifier that combines a voting mechanism to eliminate overfitting. The ensemble classifier consists of a random forest, a support vector machine classifier, and a logistic regression hyper-learner. Our novel approach was tested on two benchmarked datasets. The first data set scored F1 between 0.95 and 1.0 at the same time. The second dataset registered F1 between 0.96 and 1.0 in each of the 20 date classes. Some dates are close to each other in texture, resulting in high false positives or recall, causing a lower F1 score accuracy degree. The novelty of this approach comes from the featured representative of each date class, relying on the texture of the fruit as a discriminative feature, not on the fruit shape or color, which may not be robust enough as distinguishable features, especially in date classes that are close to each other in shape. Doi: 10.28991/HIJ-2024-05-02-010 Full Text: PDF

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Section: Related Workmentioning

confidence: 99%

Section: ) Precisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Innovative Date Fruit Classifier Based on Scatter Wavelet and Stacking Ensemble

Al-Kharaz,

Alwahhab,

Sabeeh

2024

HighTech. Innov. J.

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“…They claimed that they achieved 100%. In HW/SW co-design [28], the authors employed the ANN algorithm to classify the self-built dataset, consisting of 600 images of 6 classes. The classification accuracy achieved was 97.26%.…”

Section: Research Gap Findingmentioning

confidence: 99%

A Novel Classification Model of Date Fruit Dataset Using Deep Transfer Learning

et al. 2023

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Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%.

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