Wheat kernels classification using visible-near infrared camera based ondeep learning

Kemal ÖZKAN, (Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümü, Eskişehir, Türkiye)
Erol SEKE, (Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Eskişehir, Türkiye)
Şahin IŞIK (Eskişehir Osmangazi Üniversitesi, Mühendislik-Mimarlık Fakültesi, Bilgisayar Mühendisliği Bölümü, Eskişehir, Türkiye)
Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi
1 1
Yıl: 2021 Cilt: 27 Sayı: 5 Sayfa Aralığı: 618 - 626 Metin Dili: İngilizce DOI: 10.5505/pajes.2020.80774 İndeks Tarihi: 06-02-2022

Wheat kernels classification using visible-near infrared camera based ondeep learning

Öz:
This paper presents a smart machine learning system for classificationof hyperspectral wheat data based on deep learning methodology. Forthis purpose, the performances of AlexNet and VGG16 models wereinvestigated for the classification of hyperspectral wheat samples. Inthis study, the Support Vector Machine (SVM) and Softmax classifierswere carried out to predict labels of wheat kernels. In order to evaluatethe system performance, a new hyperspectral wheat test dataset wasconstructed using Visible-Near Infrared images (VNIR) including 50wheat species with 220 images per specimen, as 11000 samples in total.With experiments applied on newly created test dataset, overallapproximated accuracy rates of 96.00% and 99.00% determined bylinear SVM classifier, in case of fully connected layer (FC6 and FC7)features for AlexNet and VGG16, respectively. From the Softmaxpredictions, the 92% and 70% of samples were correctly discriminatedbased on trained VGG16 and AlexNet models, respectively. The obtainedsuperior results show that using a deep Convolutional Neural Networks(CNN) architecture is more efficient by the means of accuratediscrimination of wheat species. The proposed deep learning basedcategorization system promises high accuracy results for the qualityanalysis, classification and disease detection in food.
Anahtar Kelime:

Derin öğrenmeye dayalı görünür yakın kızılötesi kamera kullanılarak buğday sınıflandırması

Öz:
Bu makale, derin öğrenme metodolojisine dayalı hiperspektral buğdayverilerinin sınıflandırılması için akıllı bir makine öğrenme sistemisunmaktadır. Bu amaçla, hiperspektral buğday örneklerininsınıflandırılması için AlexNet ve VGG16 modellerinin performanslarıaraştırılmıştır. Bu çalışmada, buğday çekirdeklerinin türlerini tahminetmek için Destek Vektör Makinesi (DVM) ve Softmax sınıflandırıcılarıkullanılmıştır. Sistem performansını değerlendirmek için, GörünürYakın Kızılötesi Görüntüleme (VNIR) kullanılarak 50 buğday türüne aittür başına 220 görüntü toplamda 11000 örnek içeren yeni birhiperspektral buğday test veri kümesi oluşturulmuştur.Yeni oluşturulan test veri seti üzerinde yapılan deneylerde, AlexNet veVGG16 için tamamen bağlı katman (FC6 ve FC7) özellikleri kullanılmasıdurumunda doğrusal DVM sınıflandırıcısı tarafından belirlenenyaklaşık %96.00 ve % 99.00'lık genel doğruluk oranları elde edilmiştir.Softmax sınıflandırıcı ile numunelerin sırasıyla %92 ve %70'i, eğitimliVGG16 ve AlexNet modellerine göre doğru bir şekilde ayırtedilebilmiştir. Elde edilen üstün sonuçlar, derin bir Evrişimsel SinirAğları (ESA) mimarisi kullanmanın, buğday türlerinin doğru bir şekildeayırt edilmesi yoluyla daha verimli olduğunu göstermektedir. Önerilenderin öğrenme temelli sınıflandırma sistemi, gıdalarda kalite analizi,sınıflandırma ve hastalık tespiti için yüksek doğrulukta sonuçlar vaatetmektedir.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA ÖZKAN K, SEKE E, IŞIK Ş (2021). Wheat kernels classification using visible-near infrared camera based ondeep learning. , 618 - 626. 10.5505/pajes.2020.80774
Chicago ÖZKAN Kemal,SEKE Erol,IŞIK Şahin Wheat kernels classification using visible-near infrared camera based ondeep learning. (2021): 618 - 626. 10.5505/pajes.2020.80774
MLA ÖZKAN Kemal,SEKE Erol,IŞIK Şahin Wheat kernels classification using visible-near infrared camera based ondeep learning. , 2021, ss.618 - 626. 10.5505/pajes.2020.80774
AMA ÖZKAN K,SEKE E,IŞIK Ş Wheat kernels classification using visible-near infrared camera based ondeep learning. . 2021; 618 - 626. 10.5505/pajes.2020.80774
Vancouver ÖZKAN K,SEKE E,IŞIK Ş Wheat kernels classification using visible-near infrared camera based ondeep learning. . 2021; 618 - 626. 10.5505/pajes.2020.80774
IEEE ÖZKAN K,SEKE E,IŞIK Ş "Wheat kernels classification using visible-near infrared camera based ondeep learning." , ss.618 - 626, 2021. 10.5505/pajes.2020.80774
ISNAD ÖZKAN, Kemal vd. "Wheat kernels classification using visible-near infrared camera based ondeep learning". (2021), 618-626. https://doi.org/10.5505/pajes.2020.80774
APA ÖZKAN K, SEKE E, IŞIK Ş (2021). Wheat kernels classification using visible-near infrared camera based ondeep learning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(5), 618 - 626. 10.5505/pajes.2020.80774
Chicago ÖZKAN Kemal,SEKE Erol,IŞIK Şahin Wheat kernels classification using visible-near infrared camera based ondeep learning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 27, no.5 (2021): 618 - 626. 10.5505/pajes.2020.80774
MLA ÖZKAN Kemal,SEKE Erol,IŞIK Şahin Wheat kernels classification using visible-near infrared camera based ondeep learning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol.27, no.5, 2021, ss.618 - 626. 10.5505/pajes.2020.80774
AMA ÖZKAN K,SEKE E,IŞIK Ş Wheat kernels classification using visible-near infrared camera based ondeep learning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2021; 27(5): 618 - 626. 10.5505/pajes.2020.80774
Vancouver ÖZKAN K,SEKE E,IŞIK Ş Wheat kernels classification using visible-near infrared camera based ondeep learning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2021; 27(5): 618 - 626. 10.5505/pajes.2020.80774
IEEE ÖZKAN K,SEKE E,IŞIK Ş "Wheat kernels classification using visible-near infrared camera based ondeep learning." Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27, ss.618 - 626, 2021. 10.5505/pajes.2020.80774
ISNAD ÖZKAN, Kemal vd. "Wheat kernels classification using visible-near infrared camera based ondeep learning". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 27/5 (2021), 618-626. https://doi.org/10.5505/pajes.2020.80774
Sayfa Oluşturulma Tarihi
17-04-2024 23:15

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