Classification of pistachios with deep learning and assessing the effect of various datasets on accuracy

Aktaş, Hakan; Kızıldeniz, Tefide; Ünal, Zeynep

doi:10.1007/s11694-022-01313-5

Cited by 14 publications

(7 citation statements)

References 59 publications

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“…Through the above comparative tests, it can be preliminarily judged that the key point that has a great impact on the radiation emission of the electric drive system is whether there is a torque, essentially whether the motor controller has a power output, which has little relation with the motor speed and bus voltage [24]. In addition to the influence of whether or not the torque has on radiation emission, the torque size is further compared, and 50 Nm, 100 Nm, and 200 Nm are, respectively, set for comparison, which is found that the increase of torque has no obvious influence on radiation emission.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

New Energy Vehicle Electromagnetic Compatibility Test and Closed-Loop Simulation Analysis

2022

Journal of Sensors

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With the rapid development of new energy vehicles, electromagnetic compatibility of new energy vehicles has been widely concerned. Therefore, based on the conducted electromagnetic interference theory of electric vehicle electric drive system, a kind of electromagnetic interference transmission path is proposed, and its closed-loop simulation is carried out. The results show that the key point that has great influence on the radiation emission of the electric drive system is whether there is a torque, essentially whether the motor controller has power output, and it has little relation with the motor speed and bus voltage. In addition to the influence of whether or not the torque has on radiation emission, the torque size is further compared, set 50 Nm, 100 Nm, and 200 Nm, respectively, for comparison, and it is found that the increase of torque has no obvious influence on radiation emission. Therefore, during the test, the appropriate torque can be selected according to the actual situation, rather than the maximum torque that the component can work.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

New Energy Vehicle Electromagnetic Compatibility Test and Closed-Loop Simulation Analysis

2022

Journal of Sensors

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“…With the AlexNet model, they achieved a success rate of 94.42%, with the VGG16 model coming in at 98.54 %, and with the VGG19 model coming in at 98.14%. Aktaş et al (2022) examined the impact of different datasets on accuracy in pistachio deep learning categorization. They imply that the test accuracy was computed as 100% when training and testing the AlexNet structure with this desktop dataset.…”

Section: Discussionmentioning

confidence: 99%

Low-Cost Classification of Close and Open Shell Antep Pistachio Nuts based on Image Analysis and Machine Learning

Beyaz

2024

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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The effectiveness of post-harvest industrial processes is critical to maintaining the economic worth of pistachio nuts, which play an essential role in the agricultural economy. To achieve this level of efficiency, updated applications and technology for pistachio separation and categorization are required. Different pistachio species target different markets, highlighting the need for pistachio species classification. This work aims to develop a classification model that is distinct from existing separation approaches, based on image processing and machine learning, and can provide the required categorization. A computer vision application was done to identify between three types of pistachios. A high-resolution camera was used to capture 385 images of these pistachios. The photos of the pistachio samples were processed using image processing techniques like segmentation and feature extraction. On the given dataset, an advanced classifier based on Decision Tree and Random Forest predictions was constructed, as well as a simple and successful classifier. In the research, an application with feature extraction based on the dimension and pixel measurement is proposed. The proposed approach attained a classification success rate of 100% at 70% train and 30% test, and also, 80% train and 20% test data rate with Random Forest prediction, according to the experimental data. The provided high-performance classification model fills an important demand for the separation of pistachio types while increasing the economic worth of the species.

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“…Ozkan et al (2021) created a pistachio dataset with 16 attributes and employed a K-nearest neighbor classifier to achieve a classification accuracy of 94.18% for pistachio variety classification. Aktas ¸et al (2022) trained a pistachio image dataset using AlexNet and Inception V3, achieving an accuracy rate of over 96%. Furthermore, Singh et al (2022) employed three different convolutional neural network (CNN) models (AlexNet, VGG16, and VGG19) to classify two types of pistachio images.…”

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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Classification of pistachios with deep learning and assessing the effect of various datasets on accuracy

Cited by 14 publications

References 59 publications

New Energy Vehicle Electromagnetic Compatibility Test and Closed-Loop Simulation Analysis

New Energy Vehicle Electromagnetic Compatibility Test and Closed-Loop Simulation Analysis

Low-Cost Classification of Close and Open Shell Antep Pistachio Nuts based on Image Analysis and Machine Learning

The pistachio quality detection based on deep features plus unsupervised clustering

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