Classification of Orange Features for Quality Assessment Using Machine Learning Methods

Talha Alperen Cengel; Bunyamin Gencturk; Elham Yasin; Müslüme Beyza Yıldız; Ilkay Cinar; Osman Özbek; Murat Koklu

Makale detayları

Classification of Orange Features for Quality Assessment Using Machine Learning Methods

Yazarlar:

Talha Alperen Cengel | Bunyamin Gencturk | Elham Yasin | Müslüme Beyza Yıldız | Ilkay Cinar | Osman Özbek | Murat Koklu

Yayıncı:

Selcuk University

Ana Başlıklar:

Hassas Tarım Teknolojileri

Dergi:

Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.403-413

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001333552

pISSN:

2458-8377

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

Classification of Orange Features for Quality Assessment Using Machine Learning Methods

Yazarlar:

Talha Alperen Cengel | Bunyamin Gencturk | Elham Yasin | Müslüme Beyza Yıldız | Ilkay Cinar | Osman Özbek | Murat Koklu

Yayıncı:

Selcuk University

Ana Başlıklar:

Hassas Tarım Teknolojileri

Dergi:

Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.403-413

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001333552

pISSN:

2458-8377

Artificial Inteeligence

Quality Classification

Orange Quality

Machine Learning AlgorithmsYapay Zeka

Kalite Sınıflandırma

Portakal Kalitesi

Makine Öğrenmesi Algoritmaları

Sayı

Makale detayları

Yazarlar: Talha Alperen Cengel | Bunyamin Gencturk | Elham Yasin | Müslüme Beyza Yıldız | Ilkay Cinar | Osman Özbek | Murat Koklu

Yayıncı: Selcuk University

Ana Başlıklar: Hassas Tarım Teknolojileri

Dergi: Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.403-413

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001333552

pISSN: 2458-8377

Sayı

Yazarlar:

Talha Alperen Cengel | Bunyamin Gencturk | Elham Yasin | Müslüme Beyza Yıldız | Ilkay Cinar | Osman Özbek | Murat Koklu

Yayıncı:

Selcuk University

Ana Başlıklar:

Hassas Tarım Teknolojileri

Dergi:

Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.403-413

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001333552

pISSN:

2458-8377