Deep Learning-Based Classification Of the Defective Pistachios Via Deep Autoencoder Neural Networks

Abbaszadeh, Mehdi; Aliakbar, Rahimifard,; Eftekhari, Mohammadali; Zadeh, Hossein Ghayoumi; Fayazi, Ali; Ali, Dini Salmiyah Fithrah; Danaeian, Mostafa

doi:10.48550/arxiv.1906.11878

Cited by 3 publications

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“…In their study, Abbaszadeh et al used deep auto-encoder neural networks to classify pistachios as defective and flawless. As a result of the study, a classification accuracy of 80.3% was obtained in the detection of defective pistachios [16].…”

Section: Related Workmentioning

confidence: 87%

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Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models

et al. 2022

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Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and exported in Turkey. Since the prices, tastes, and nutritional values of these types differs, the type of pistachio becomes important when it comes to trade. This study aims to identify these two types of pistachios, which are frequently grown in Turkey, by classifying them via convolutional neural networks. Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer vision system. The pre-trained dataset includes a total of 2148 images, 1232 of Kirmizi type and 916 of Siirt type. Three different convolutional neural network models were used to classify these images. Models were trained by using the transfer learning method, with AlexNet and the pre-trained models VGG16 and VGG19. The dataset is divided as 80% training and 20% test. As a result of the performed classifications, the success rates obtained from the AlexNet, VGG16, and VGG19 models are 94.42%, 98.84%, and 98.14%, respectively. Models’ performances were evaluated through sensitivity, specificity, precision, and F-1 score metrics. In addition, ROC curves and AUC values were used in the performance evaluation. The highest classification success was achieved with the VGG16 model. The obtained results reveal that these methods can be used successfully in the determination of pistachio types.

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Section: Related Workmentioning

confidence: 87%

“…When the literature is examined, there are cases of pistachios being open and closed shell [7,20], being defective [14,16], classification of pistachios [15], and classification of pistachio varieties by machine learning [21]. Only one of these studies is related to our study [21].…”

Section: Discussionmentioning

confidence: 99%

Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models

et al. 2022

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“…In a different study, Abbaszadeh et al used deep auto-encoder neural networks to divide peanuts into two different classes as problematic and unproblematic. As a result of this study, they reported that they achieved 80.3% correct classification success of problematic pistachios (Abbaszadeh et al, 2019). Rahimzadeh and Attar developed an image-processing-based system to determine whether different peanut species are open-mouthed or closedmouthed.…”

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confidence: 99%

Classification Of Pistachio Images With The ResNet Deep Learning Model

Avuçlu

2023

SJAFS

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Pistachio, which is grown in many parts of the world today, has an important place in the agricultural economy. In order to maintain this economic value, the post-harvest industrial classification process is very important to obtain efficiency from this harvest. In the process of separating pistachios, an efficient classification process is needed in order for different pistachio species to appeal to different markets. For this reason, the classification process of pistachios is very important. In this study, Kirmizi and Siirt pistachio classification with 2148 images was made using ResNet architecture. After the statistical experimental studies, the highest classification accuracy was obtained from fold-1 as 88.5781% and the Accuracy value was 0.86168 after the classification process.

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Rapid detection of adulteration in pistachio based on deep learning methodologies and affordable system

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et al. 2023

Multimed Tools Appl

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Deep Learning-Based Classification Of the Defective Pistachios Via Deep Autoencoder Neural Networks

Cited by 3 publications

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Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models

Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models

Classification Of Pistachio Images With The ResNet Deep Learning Model

Rapid detection of adulteration in pistachio based on deep learning methodologies and affordable system

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