Kalite Değerlendirmesi için Portakal Özelliklerinin Makine Öğrenimi ile Sınıflandırılması

Abstract

Portakallar narenciye ailesinin bir üyesidir ve yüksek C vitamini içeriği, tatlı ve ekşi tadı ve yararlı lif ve antioksidan nitelikleri nedeniyle büyük miktarlarda yenir. Portakalda kalite güvencesi, pazarda rekabet gücü ve müşteri memnuniyeti için çok önemlidir. Kaliteyi değerlendirmeye yönelik geleneksel yaklaşımlar maliyetlidir ve insanlar tarafından yapılan hatalara açıktır. Bu araştırma, farklı makine öğrenimi algoritmalarının portakal kalitesi değerlendirme prosedürünü ne kadar iyi otomatikleştirdiğini ve geliştirdiğini araştırmayı amaçlamaktadır. Rastgele Orman (RF), XGBoost ve k-En Yakın Komşular (k-NN) algoritmalarının etkinliğini değerlendirmek için 241 örnek ve 11 özellik (boyut, ağırlık, tatlılık (Brix), asitlik (pH) ve renk) içeren bir veri kümesi kullanılmıştır. Bulgulara göre, k-NN %69,38 ile en yüksek doğruluğu elde ederken, RF %67,34 ile ikinci ve XGBoost %63,26 ile üçüncü sırada yer aldı. Bu sonuçlar, makine öğrenimi modellerinin daha güvenilir ve etkili bir yaklaşım sunarak portakal endüstrisinde kalite kontrolünü iyileştirmek için nasıl kullanılabileceğini göstermektedir. Bu çalışmaya göre, makine öğrenimi portakallar için kalite kontrol prosedürlerini büyük ölçüde geliştirebilir, bu da müşteriler için daha yüksek kaliteli ürünler ve tedarikçiler için daha fazla üretkenlikle sonuçlanabilir. Portakal sektörü bu teknolojileri kullanarak ürün kalitesini artırabilir ve işlemleri hızlandırabilir; bu da sonuçta hem üreticilere hem de tüketicilere fayda sağlayacaktır.

Keywords

• Yapay Zeka
• Kalite Sınıflandırma
• Portakal Kalitesi
• Makine Öğrenmesi Algoritmaları

Classification of Orange Features for Quality Assessment Using Machine Learning Methods

Abstract

Oranges are a member of the citrus family and are eaten in large quantities due to their high vitamin C content, sweet and tart taste, and useful fiber and antioxidant qualities. Orange quality assurance is essential to market competitiveness and customer satisfaction. Conventional approaches to evaluating quality are costly and susceptible to mistakes made by people. This research aims to investigate how well different machine learning algorithms automate and improve the orange quality assessment procedure. A dataset containing 241 samples and 11 features (size, weight, sweetness (Brix), acidity (pH), and color) was used to evaluate the effectiveness of the Random Forest (RF), XGBoost, and k-Nearest Neighbors (k-NN) algorithms. According to the findings, k-NN acquired the maximum accuracy of 69.38%, with RF coming in second at 67.34% and XGBoost third at 63.26%. These results demonstrate how machine learning models may be used to improve quality control in the orange industry by offering a more dependable and effective approach. According to this study, machine learning can greatly improve the quality control procedures for oranges, resulting in higher-quality goods for customers and more productivity for providers. The orange sector can enhance product quality and expedite operations by utilizing these technologies, which will eventually benefit both producers and consumers.

Keywords

• Artificial Inteeligence
• Quality Classification
• Orange Quality
• Machine Learning Algorithms

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