Discrimination of sunflower seeds using multispectral and texture dataset in combination with region selection and supervised classification methods

Bantan, Rashad A. R.; Ali, Aqib; Naeem, Samreen; Jamal, Farrukh; Elgarhy, Mohammed; Chesneau, Christophe

doi:10.1063/5.0024017

Cited by 14 publications

(10 citation statements)

References 24 publications

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“…For feature reduction, the selected features have been replaced by a lower dimension. Instead of a low-dimensional feature, it retains the original data structure as much as possible [25]. The low-dimensional feature space also reduces the time and cost of execution, and the results obtained are almost comparable to the original feature space.…”

Section: Feature Reductionmentioning

confidence: 98%

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

Ali¹,

Mashwani²,

Naeem³

et al. 2021

Computers, Materials &Amp; Continua

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The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work

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Section: Feature Reductionmentioning

confidence: 98%

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

Ali¹,

Mashwani²,

Naeem³

et al. 2021

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

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“…Numerous researchers have developed ways of automating the error-prone practice of seed testing. Currently, traditional methods are used to identify seeds since they are automated and generate more reliable data [14][15][16]. Several stated methods, on the other hand, use color-based thresholds to anticipate elements such as the perimeter, roundness and color values, width, and perimeter in order to interpret the seed [17].…”

Section: Related Workmentioning

confidence: 99%

Germination Quality Prognosis: Classifying Spectroscopic Images of the Seed Samples

Alotaibi¹

2023

Intelligent Automation &Amp; Soft Computing

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One of the most critical objectives of precision farming is to assess the germination quality of seeds. Modern models contribute to this field primarily through the use of artificial intelligence techniques such as machine learning, which present difficulties in feature extraction and optimization, which are critical factors in predicting accuracy with few false alarms, and another significant difficulty is assessing germination quality. Additionally, the majority of these contributions make use of benchmark classification methods that are either inept or too complex to train with the supplied features. This manuscript addressed these issues by introducing a novel ensemble classification strategy dubbed "Assessing Germination Quality of Seed Samples (AGQSS) by Adaptive Boosting Ensemble Classification" that learns from quantitative phase features as well as universal features in greyscale spectroscopic images. The experimental inquiry illustrates the significance of the proposed model, which outperformed the currently available models when performance analysis was performed.

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“…The frequency domain features, known as spectral features, are used in texture analysis. These features are calculated as power of different areas (A) also known as rings [26]. The numerical explanation is given as…”

Section: Spectral Featuresmentioning

confidence: 99%

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Naeem¹,

Ali

Chesneau

et al. 2021

Agronomy

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This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

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Discrimination of sunflower seeds using multispectral and texture dataset in combination with region selection and supervised classification methods

Cited by 14 publications

References 24 publications

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

Germination Quality Prognosis: Classifying Spectroscopic Images of the Seed Samples

The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

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