Classification of Orange Features for Quality Assessment Using Machine Learning Methods
Yazarlar (7)
Talha Alperen Cengel
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Bunyamin Gencturk
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Muslume Beyza Yildiz
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ilkay Cinar Selçuk Üniversitesi, Türkiye
Doç. Dr. Osman Ozbek Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale)
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| Dergi Adı | Selcuk Journal of Agriculture and Food Sciences | ||
| Dergi ISSN | 2458-8377 | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2024 |
| Kabul Tarihi | 16-12-2024 | Yayınlanma Tarihi | 16-12-2024 |
| Cilt / Sayı / Sayfa | 38 / 3 / 403–413 | DOI | 10.15316/SJAFS.2024.036 |
| Makale Linki | https://dergipark.org.tr/tr/pub/selcukjafsci/issue/88545/1506747 | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
Görüntü İşleme
Yapay Zeka
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| 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. |
| Anahtar Kelimeler |
| Artificial Intelligence | Quality Classification | Orange Quality | Machine Learning Algorithms |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Google Scholar | 13 |