Data/hora: 13/06/2024 04:42:36

Provedor de dados: | 111
País: | China
Título: | Freshness and quality assessment of parsley using image processing and artificial intelligence techniques
Autores: | Hosseinpour Zarnaq, Mohammad; Omid, Mahmoud; Soltani Firouz, Mahmoud; Jafarian, Mostafa; Bazyar, Pourya.
Data: | 2022-06-28
Ano: | 2022
Resumo: | Fruits and&amp;nbsp;vegetables&amp;nbsp;are&amp;nbsp;important&amp;nbsp;components of healthy diets. Vegetable freshness is important for both postharvest industry and consumer appeal. This study focused on freshness detection of parsleys using combined image processing and artificial intelligence techniques. A dataset of color and texture features computed from parsley images. Linear discriminant analysis (LDA) and principal component analysis (PCA) methods are used for feature reduction. Multilayer perceptron (MLP) neural networks, support vector machine (SVM) and decision trees (DTs) classifiers were used for classification. Results showed MLP with LDA feature selection methods had higher performance and the overall accuracy, RMSE, MAE of MLP classifier (using LDA feature selection) were 97.22%, 0.17, and 0.03, respectively. This approach provided a rapid and nondestructive detection of parsley freshness without using chemical or colorimetric analysis. The results demonstrated that suggested approach could be employed satisfactorily for inspection, classification and automation of vegetables postharvest operations.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.cigrjournal.org/index.php/Ejounral/article/view/7687
Editor: | International Commission of Agricultural and Biosystems Engineering
Relação: | http://www.cigrjournal.org/index.php/Ejounral/article/view/7687/3891
Formato: | application/pdf

Fonte:

Agricultural Engineering International: CIGR Journal; Vol. 24 No. 2 (2022): CIGR Journal

1682-1130

Fonte: | Copyright (c) 2022 Agricultural Engineering International: CIGR Journal