Nondestructive classification of saffron using color and textural analysis

Moghadam, Morteza Mohamadzadeh; Taghizadeh, Masoud; Sadrnia, Hassan; Pourreza, Hamid Reza

doi:10.1002/fsn3.1478

Cited by 12 publications

(6 citation statements)

References 28 publications

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“…In SVMs, the input data space is mapped into a high-dimensional feature space via a kernel function using a minimum of training data. Other model types used in this group include linear SVMs, quadratic SVMs, fine Gaussian SVMs, medium Gaussian SVMs and coarse Gaussian SVMs [46], [47].…”

Section: B Classification By Gnbmentioning

confidence: 99%

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

Idrissi,

Derouich,

Mahfoud

et al. 2024

IEEE Access

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Identifying stator imbalance problems in induction machines (IMs) is crucial to preserving operating efficiency, reliability, and safety. This identification contributes to the IM's overall health, prolongs its lifespan, and helps organizations meet regulatory and performance standards. Moreover, it enables the execution of maintenance tasks when required, improving resource use and decreasing shutdown periods. This research presents a Non-Destructive Technique (NDT) that utilizes a thermography-based machine learning algorithm for diagnosing Stator Imbalance Defects (SID) within IMs experimentally. This technique relies on assessing temperature distribution on the outer surface of the IM stator. Then, the evaluation of various statistical variables that provide valuable insights into the normality of temperature distribution within the designated area referred to as the Region of Interest (ROI) is presented. Initially, thermal images of the stator are captured under various operating conditions, including a healthy state, SID presence, and phase missing. Then, all thermal images are converted into thermographic data matrices for statistical analysis using the Machine Learning Algorithm Gaussian Naïve Bayes (GNB). This analysis aimed at classifying SID in IMs arising from deviations in the normalcy of temperature distribution within the ROI. To demonstrate the efficacy of the suggested GNB approach, a comparison with the Subspace Discriminant Classifier and Cubic Support Vector Machine is provided. The GNB algorithm exhibits exceptional accuracy of 95.2% in classification tasks while maintaining a minimal processing time, irrespective of the operational duration of the IM.

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Section: B Classification By Gnbmentioning

confidence: 99%

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

Idrissi,

Derouich,

Mahfoud

et al. 2024

IEEE Access

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“…Additionally, Moghadam et al [ 21 ], developed a Saffron classification-based model using machine vision techniques for detecting the authenticity of saffron. The experts of Iran categorize saffron into 3 classes such as, Sargol, Pushal and, Negin,.…”

Section: Related Workmentioning

confidence: 99%

SaffNet: an ensemble-based approach for saffron adulteration prediction using statistical image features

Amin

Selwal

Sabha

2023

Multimed Tools Appl

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Saffron is one of the costlier spices that are cultivated in specific regions of the world. Due to its restricted accessibility and more popularity, eventually saffron adulteration is one of the concerning issues in the recent times. It becomes difficult for human vision to discriminate between real and adulterated saffron samples. With the emergence of visual computing and data-driven algorithms, the saffron adulteration prediction systems (SAPS) are designed to predict the original and adulterated saffron samples. However, the majority of the techniques exhibit promising performance but the problem of generalization capabilities (unseen – samples) and scarcity of the saffron databases are still open research challenges. In this work, to overcome these issues, we propose a novel ensemble-based saffron prediction model (SaffNet) using statistical image features for the detection of contamination in the Kashmiri saffron. As data-driven approaches mainly rely on the representative samples, but to the best of our knowledge the standard benchmark datasets for Kashmiri saffron is not available. Therefore, we have created our novel Saffron dataset (Saff-Kash) collected afresh from different parts of Kashmir valley that includes the samples of both the authentic and adulterated saffron classes. The primary aim of the work is to anticipate the adulteration in saffron samples. Thereafter, these images are pre-processed and the dataset is prepared for the proposed SaffNet model. The SaffNet architecture designed using gradient boosting ensemble evaluated on Saff-Kash outperforms the outcomes of individual classifiers i.e., Support vector machine (SVM), decision tree, and K-Nearest neighbor (KNN) with an overall accuracy of 98%. Moreover, the execution time taken by the SaffNet model for training the SVM classifier is 8.56 milliseconds whereas for gradient boosting classifier it is 7.7 milliseconds.

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“…With recent advances in machine vision in terms of accuracy, robustness, and affordability, this technology has become suitable for determining the quality of saffron [11]. For example, Aghaei et al [12] used machine vision technology to evaluate different saffron drying methods.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Aghaei et al [12] used machine vision technology to evaluate different saffron drying methods. Mohamadzadeh Moghadam et al [11] used machine vision to classify different parts of saffron stigma (Pushal, Negin, and Sargol). Minaei et al [13] used different color spaces to classify 33 samples of saffron from different geographical regions of Iran.…”

Section: Introductionmentioning

confidence: 99%

“…Various algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), self-organizing maps (SOM), multilayer perceptron (MLP), radial basis function neural network (RBF), learning vector quantization (LVQ), principal component analysis (PCA), etc., are used for classification. A wide range of research has been done employing these algorithms, such as the classification of rice grains [23] and wheat cultivars [24] using MLP neural network; classification of plant species using SVM [25]; classification of the dikarya fungi using SVM, KNN, and SOM [26]; sorting different parts of saffron with 22 different classifiers (SVM and KNN models) [11]; and classification of saffron and fake samples using PCA and PLS [27].…”

Section: Introductionmentioning

confidence: 99%

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Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing

Alighaleh

Pakdel

Ghooshkhaneh

et al. 2023

Foods

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Saffron (Crocus sativus L.) is the most expensive spice in the world, known for its unique aroma and coloring in the food industry. Hence, its high price is frequently adulterated. In the current study, a variety of soft computing methods, including classifiers (i.e., RBF, MLP, KNN, SVM, SOM, and LVQ), were employed to classify four samples of fake saffron (dyed citrus blossom, safflower, dyed fibers, and mixed stigma with stamens) and three samples of genuine saffron (dried by different methods). RGB and spectral images (near-infrared and red bands) were captured from prepared samples for analysis. The amount of crocin, safranal, and picrocrocin were measured chemically to compare the images’ analysis results. The comparison results of the classifiers indicated that KNN could classify RGB and NIR images of samples in the training phase with 100% accuracy. However, KNN’s accuracy for different samples in the test phase was between 71.31% and 88.10%. The RBF neural network achieved the highest accuracy in training, test, and total phases. The accuracy of 99.52% and 94.74% was obtained using the features extracted from RGB and spectral images, respectively. So, soft computing models are helpful tools for detecting and classifying fake and genuine saffron based on RGB and spectral images.

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Nondestructive classification of saffron using color and textural analysis

Cited by 12 publications

References 28 publications

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

Stator Imbalance Defects Diagnosis of Induction Machine Using Thermography and Machine Learning Algorithms

SaffNet: an ensemble-based approach for saffron adulteration prediction using statistical image features

Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing

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