Acoustic signal-based deep learning approach for smart sorting of pistachio nuts

Hosseinpour-Zarnaq, Mohammad; Omid, Mahmoud; Taheri‐Garavand, Amin; Nasiri, Alireza; Mahmoudi, Asghar

doi:10.1016/j.postharvbio.2021.111778

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…The results of this experiment demonstrated that the proposed method had superior performance. Hosseinpour‐Zarnaq et al (2022) dropped open‐shelled and closed‐shelled pistachios on a plate and analyzed the sound signal to classify the pistachios based on the trait of the sound signal. However, as an agricultural product with small particles, pistachios have a relatively weak collision sound signal that is more susceptible to environmental noise.…”

Section: Introductionmentioning

confidence: 99%

The pistachio quality detection based on deep features plus unsupervised clustering

Liu,

Yang,

Zhu

et al. 2024

J Food Process Engineering

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The opening condition of the pistachio is a trait that has been sorted by machine vision. This study presented a qualitative and quantitative method for pistachio opening detection based on unsupervised learning. First, the convolution neural network ResNet18 was utilized to extract the pistachio features based on the different layers: bn4b_branch2b (bn4b), bn5b_branch2b (bn5b), and fc1000. The high‐dimensional feature was mapped to the low‐dimensional by t‐distributed stochastic neighbor embedding algorithm, and then, the clustering algorithms were utilized to cluster the image feature under different spatial dimensions. It was best that the clustering performance came from the bn5b feature layer. The accuracy of this method was 99.31% in three‐dimensional space. And completed the analysis of pistachio opening degree. This method exhibits significant potential for enhancing the quality detection and product classification of pistachios. The unsupervised clustering is utilized to sort not only pistachios but also general nuts.Practical ApplicationsIn this study, we proposed a quality detection method for pistachios based on unsupervised learning, which was capable of performing both qualitative and quantitative analyses of pistachio opening. This method provides an efficient quality detection and product classification strategy, with important practical value. The unsupervised clustering is utilized to sort not only pistachios but also general nuts. Furthermore, it significantly reduces the consumption of manpower and material resources, paving the way for the development of intelligent sorting equipment in the future, and has reference significance for the quality detection of other agricultural products.

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Section: Introductionmentioning

confidence: 99%

The pistachio quality detection based on deep features plus unsupervised clustering

Liu,

Yang,

Zhu

et al. 2024

J Food Process Engineering

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“…Acoustic signals have been the subject of numerous researches that have attempted to classify the quality and maturity of various fruits such as durian (Kharamat et al, 2020), coconut (Rahmawati et al, 2019;Caladcad et al, 2020;Fadchar and Cruz, 2020), cacao (Bueno et al, 2020), watermelon (Chawgien and Kiattisin, 2021), pistachio nut (Hosseinpour et al, 2022), and pineapple (Huang et al, 2022;Phawiakkharakun et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Enhanced non-destructive of degree of pineapple juiciness using ensemble learning model based on tapping sound sensing

Phawiakkharakun,

Pongpinigpinyo

2024

Int. J. Appl. Sci. Eng.

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This research proposes to enhance the non-destructive method for classifying the juiciness of pineapples using tapping sound sensing. Ten statistical features were extracted from the waveform signals by waveform analysis. These features were then separated into a spectral feature set (3 features) and a temporal feature set (7 features). Each feature set was calculated with the weight of important features and selected features for 15 training datasets using 10 machine learning classifiers. Ten machine learning classifiers were Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Ensemble Voting, Adaboost, Ensemble Bagging, and Ensemble Stacking. The classifiers were evaluated with accuracy and the kappa coefficient. Grid search was used to determine various important hyperparameters for Machine Learning classifiers. The experiment results showed that the Ensemble Voting (soft), Ensemble Stacking, and MLP outperformed other classifiers. They can obtain an accuracy of 92.08%, and kappa coefficients are 0.8811, 0.8808 and 0.8808, respectively.

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“…The results showed that the correlation coefficient between sound velocity and watermelon ripeness (sugar content) was .97. In the aspect of detecting the defects of agricultural products by acoustic technology, scholars have studied the crack detection on the surface of poultry eggs (István et al, 2021; Sun et al, 2020), the hollow detection (Khakrangin et al, 2021; Mao et al, 2015) and the opening and closing detection of pistachios (Mohammad et al, 2022) and so on. These studies fully proved the feasibility of applying acoustic technology to the quality evaluation of agricultural products.…”

Section: Introductionmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

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Acoustic signal-based deep learning approach for smart sorting of pistachio nuts

Cited by 10 publications

References 29 publications

The pistachio quality detection based on deep features plus unsupervised clustering

The pistachio quality detection based on deep features plus unsupervised clustering

Enhanced non-destructive of degree of pineapple juiciness using ensemble learning model based on tapping sound sensing

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

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