Makine Öğrenme Algoritmalarına Dayalı Görüntü Özellikleri Kullanılarak Hurma Meyvelerinin Sınıflandırılması

Year 2024, Volume: 55 Issue: 1, 26 - 35, 31.01.2024

Öznur Özaltın

Abstract

Bilimsel olarak Phoenix dactylifera olarak bilinen hurma meyvesi, yüksek besin değeri ve temel vitamin ve minerallerin bolluğu nedeniyle önemli bir diyet bileşenidir. Doğal ortamında çok sayıda
varyasyon sergileyen bu meyvenin sınıflandırılmasını ayırt etme süreci, özel bir yetenek gerektirir. Tarımsal ürünlerin görüntülerine dayalı türlerin otomatik olarak tanınması son zamanlarda önemli
bir yaygınlık kazanmıştır. Bu amaçla, mevcut çalışma, yedi tür hurma meyvesini otomatik olarak tanımlamak için makine öğrenme algoritmalarını kullandı. Araştırmada hurma meyvelerinin
sınıflandırılması amacıyla farklı hiperparametreler ile karar ağaçları, K-En Yakın Komşu, yapay Sinir Ağları ve Destek Vektör Makinesi kullanılmıştır. Veri seti, eğitim ve test için sırasıyla %80 ve %20
oranında bölünmüştür ve eğitim sürecinde, fazla uydurmayı önlemek için 5 katlı çapraz doğrulama tekniği kullanılmıştır. Özetle, sonuçlar en iyi algoritmanın katman boyutu 25 olan Sinir Ağları
olduğunu göstermektedir. Bu çalışmada önerilen bu algoritma %93,85'lik bir test doğruluk oranı elde etmiştir. Araştırmada hesaplama karmaşıklığının olmaması göz önüne alındığında, çeşitli araçlara zahmetsizce dahil edilebilir, böylece hurma türlerinin tespiti kolaylaşır.

Keywords

Otomatik tespit, sınıflandırma, hurma meyveleri, makine öğrenimi algoritmaları, sinir ağları

Date Fruit Classification by Using Image Features Based on Machine Learning Algorithms

Abstract

The date fruit, scientifically known as Phoenix dactylifera, is a significant dietary component due to its high nutritional value and abundance of essential vitamins and minerals. The process of
discerning the classification of this fruit, which exhibits a multitude of variations within its natural domain, needs a specialized skill set. The automated recognition of species based on images of
agricultural goods has gained significant prevalence in recent times. In this objective, the present study employed machine learning algorithms to automatically identify seven types of date
fruit. In the investigation, decision tree, K-nearest neighbor, artificial neural networks, and support vector machine through their different hyperparameters are employed for the purpose of
classifying date fruit. The dataset was divided into ratios of 80% and 20% for training and testing, respectively, and the training process employed the five-fold cross-validation technique to avoid
overfitting. In summary, the results indicate that the best algorithm is neural network with a layer size of 25. In this study, this proposed algorithm achieved a test accuracy rate of 93.85%. Given the absence of computational complexity in the investigation, it can be effortlessly incorporated into diverse tools, thereby facilitating the identification of the types of date fruit.

Keywords

Automatic detection, classification, date fruit, machine learning algorithms, neural networks

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