Predicting moisture content of agricultural products using artificial neural networks

Drying of agricultural products is a significant process to store and use them for various purposes. There are few drying methods in agricultural industry, among them fluidized bed drying is widely employed due to its several advantages over the other methods. The prediction of drying characteristics with a small number of experiments is rather efficient since because of the fact that the drying experiments is time consuming and requires tedious work for a single agricultural product. Therefore, several methods such as deterministic, stochastic, artificial intelligence have been developed in order to predict the drying characteristics based on the experimental data obtained from the lab-scale fluidized bed drying system. In this paper, the artificial neural networks (ANN) method was used to predict the drying characteristics of agricultural products such as hazelnut, bean and chickpea. The ANN was trained using experimental data for three different products through the back propagation algorithm containing double input and single output parameters. The results showed fairly good agreement between predicted results by using ANN and the measured data taken under the same modeling conditions. The mean relative error (MRE) and mean absolute error (MAE) obtained when unknown data were applied to the networks was 3.92 and 0.033, respectively, which is very satisfactory. © 2009 Elsevier Ltd. All rights reserved.

Dergi Adı Advances in Engineering Software
Dergi Cilt Bilgisi 41
Dergi Sayısı 3
Sayfalar 464 - 470
Yayın Yılı 2010
Eser Adı
[dc.title]
Predicting moisture content of agricultural products using artificial neural networks
Yazar
[dc.contributor.author]
Topuz A.
Yayın Yılı
[dc.date.issued]
2010
Yayıncı
[dc.publisher]
Elsevier Ltd
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
Drying of agricultural products is a significant process to store and use them for various purposes. There are few drying methods in agricultural industry, among them fluidized bed drying is widely employed due to its several advantages over the other methods. The prediction of drying characteristics with a small number of experiments is rather efficient since because of the fact that the drying experiments is time consuming and requires tedious work for a single agricultural product. Therefore, several methods such as deterministic, stochastic, artificial intelligence have been developed in order to predict the drying characteristics based on the experimental data obtained from the lab-scale fluidized bed drying system. In this paper, the artificial neural networks (ANN) method was used to predict the drying characteristics of agricultural products such as hazelnut, bean and chickpea. The ANN was trained using experimental data for three different products through the back propagation algorithm containing double input and single output parameters. The results showed fairly good agreement between predicted results by using ANN and the measured data taken under the same modeling conditions. The mean relative error (MRE) and mean absolute error (MAE) obtained when unknown data were applied to the networks was 3.92 and 0.033, respectively, which is very satisfactory. © 2009 Elsevier Ltd. All rights reserved.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Artificial neural networks (ANN)
Konu Başlıkları
[dc.subject]
Bean
Konu Başlıkları
[dc.subject]
Chickpea
Konu Başlıkları
[dc.subject]
Fluidized bed drying
Konu Başlıkları
[dc.subject]
Hazelnut
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0965-9978
İlk Sayfa Sayısı
[dc.identifier.startpage]
464
Son Sayfa Sayısı
[dc.identifier.endpage]
470
Dergi Adı
[dc.relation.journal]
Advances in Engineering Software
Dergi Sayısı
[dc.identifier.issue]
3
Dergi Cilt Bilgisi
[dc.identifier.volume]
41
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.advengsoft.2009.10.003
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/7102
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
27
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
09 Şubat 2024 20:22
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Tıklayınız
drying agricultural methods characteristics products networks single artificial results predict several obtained experimental experiments fluidized showed satisfactory parameters output double containing Elsevier algorithm fairly propagation through different trained rights reserved relative unknown absolute conditions applied
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