Classification of lemon quality using hybrid model based on Stacked AutoEncoder and convolutional neural network

Agricultural product quality assessment is crucial for determining marketability and managing waste. It helps ensure that products meet industry standards and consumer expectations, leading to increased sales and reduced spoilage. It is important for growers, processors, and distributors to have sys...

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Vydáno v:European food research & technology Ročník 249; číslo 6; s. 1655 - 1667
Hlavní autoři: Yılmaz, Esra Kavalcı, Adem, Kemal, Kılıçarslan, Serhat, Aydın, Hatice Aktaş
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
Springer Nature B.V
Témata:
Agricultural products
Agriculture
Algorithms
Analytical Chemistry
Artificial intelligence
Artificial neural networks
Biotechnology
Chemistry
Chemistry and Materials Science
Classification
color
data collection
Datasets
Deep learning
Food Science
Forestry
industry
Industry standards
lemons
Machine learning
Marketability
Morphology
Neural networks
Original Paper
Quality assessment
Quality control
Spoilage
Waste management
wastes
Lemon Quality
Dimension Reduction
SAE
Deep Learning
ISSN:1438-2377, 1438-2385
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Shrnutí:Agricultural product quality assessment is crucial for determining marketability and managing waste. It helps ensure that products meet industry standards and consumer expectations, leading to increased sales and reduced spoilage. It is important for growers, processors, and distributors to have systems in place to ensure the quality of their agricultural products. When the literature is examined, it is seen that artificial intelligence is used for classification processes of many agricultural products. Many studies carried out in this context are based on processing images of agricultural products with various deep learning and machine learning methods and classifying their quality according to these results. In this study, data sets consisting of statistical properties obtained by GLCM, Color Space, and Morphological methods were combined for the first time in this study and used as a single data set. In addition, hybrid classification processes were carried out by applying dimension reduction methods, such as Stacked AutoEncoder, ReliefF, and deep learning methods, such as CNN, SVC, Ridge Classifier, and Subspace Discriminant, to the created data set. When morphological features were given as input to ML algorithms for normal classification, the SAE–CNN hybrid model we proposed in the study achieved a success above the literature with 98.96% accuracy using 32 features. The experimental results demonstrated the effectiveness of the proposed lemon classification system.
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ISSN:1438-2377
1438-2385
DOI:10.1007/s00217-023-04245-6