Derin öğrenmeye dayalı görünür yakın kızılötesi kamera kullanılarak buğday sınıflandırması

Bu makale, derin öğrenme metodolojisine dayalı hiperspektral buğday verilerinin sınıflandırılması için akıllı bir makine öğrenme sistemi sunmaktadır. Bu amaçla, hiperspektral buğday örneklerinin sınıflandırılması için AlexNet ve VGG16 modellerinin performansları araştırılmıştır. Bu çalışmada, buğday çekirdeklerinin türlerini tahmin etmek için Destek Vektör Makinesi (DVM) ve Softmax sınıflandırıcıları kullanılmıştır. Sistem performansını değerlendirmek için, Görünür Yakın Kızılötesi Görüntüleme (VNIR) kullanılarak 50 buğday türüne ait tür başına 220 görüntü toplamda 11000 örnek içeren yeni bir hiperspektral buğday test veri kümesi oluşturulmuştur. Yeni oluşturulan test veri seti üzerinde yapılan deneylerde, AlexNet ve VGG16 için tamamen bağlı katman (FC6 ve FC7) özellikleri kullanılması durumunda doğrusal DVM sınıflandırıcısı tarafından belirlenen yaklaşık %96.00 ve % 99.00'lık genel doğruluk oranları elde edilmiştir. Softmax sınıflandırıcı ile numunelerin sırasıyla %92 ve %70'i, eğitimli VGG16 ve AlexNet modellerine göre doğru bir şekilde ayırt edilebilmiştir. Elde edilen üstün sonuçlar, derin bir Evrişimsel Sinir Ağları (ESA) mimarisi kullanmanın, buğday türlerinin doğru bir şekilde ayırt edilmesi yoluyla daha verimli olduğunu göstermektedir. Önerilen derin öğrenme temelli sınıflandırma sistemi, gıdalarda kalite analizi, sınıflandırma ve hastalık tespiti için yüksek doğrulukta sonuçlar vaat etmektedir.

Anahtar Kelimeler:

Besin mühendisliği, Evrişimsel sinir ağları, Sınıflandırma, Görünür yakin kızılötesi, Buğday

Wheat kernels classification using visible-near infrared camera based on deep learning

This paper presents a smart machine learning system for classification of hyperspectral wheat data based on deep learning methodology. For this purpose, the performances of AlexNet and VGG16 models were investigated for the classification of hyperspectral wheat samples. In this study, the Support Vector Machine (SVM) and Softmax classifiers were carried out to predict labels of wheat kernels. In order to evaluate the system performance, a new hyperspectral wheat test dataset was constructed using Visible-Near Infrared images (VNIR) including 50 wheat species with 220 images per specimen, as 11000 samples in total. With experiments applied on newly created test dataset, overall approximated accuracy rates of 96.00% and 99.00% determined by linear SVM classifier, in case of fully connected layer (FC6 and FC7) features for AlexNet and VGG16, respectively. From the Softmax predictions, the 92% and 70% of samples were correctly discriminated based on trained VGG16 and AlexNet models, respectively. The obtained superior results show that using a deep Convolutional Neural Networks (CNN) architecture is more efficient by the means of accurate discrimination of wheat species. The proposed deep learning based categorization system promises high accuracy results for the quality analysis, classification and disease detection in food.

Keywords:

Food engineering, Convolutional neural network, Classification, Visible-Near infrared, Wheat,

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