Scopus
YÖKSİS DOI Eşleşti
SJR Q2
Apple (Malus domestica) Quality Evaluation Based on Analysis of Features Using Machine Learning Techniques
Applied Fruit Science · Aralık 2024
YÖKSİS Kayıtları
Apple (Malus domestica) Quality Evaluation Based on Analysis of Features Using Machine Learning Techniques
Applied Fruit Science / Erwerbs-Obstbau · 2024 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Apple (Malus domestica) Quality Evaluation Based on Analysis of Features Using Machine Learning Techniques
Applied Fruit Science Erwerbs-Obstbau · 2024 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Energy Use Efficiency and Greenhouse Gas Emissions in Plum (Prunus domestica L.) Production: Evidence from Central Türkiye
2026 ISSN: 2948-2623 SCI-Expanded
Prof. Dr. ZEKİ BAYRAMOĞLU →
Energy Use Efficiency and Greenhouse Gas Emissions in Sour Cherry (Prunus cerasus L.) Production Systems in Türkiye
2026 ISSN: 2948-2623 SCI-Expanded
Prof. Dr. ZEKİ BAYRAMOĞLU →
Energy Use and Emission Efficiency in White Cherry Production: Evidence from Türkiye
2026 ISSN: 2948-2623 SCI-Expanded
Prof. Dr. ZEKİ BAYRAMOĞLU →
Energy Use and Emission Efficiency in White Cherry Production: Evidence from Türkiye
2026 ISSN: 2948-2623 SCI-Expanded
Prof. Dr. HASAN GÖKHAN DOĞAN →
Benchmarking Analysis of CNN Models for Maturity Levels of Guavas
2026 ISSN: 2948-2623 SCI-Expanded
Öğr. Gör. AYŞEGÜL TOPRAK →
Determination of Energy Use Efficiency, Greenhouse Gas Emissions and Production Costs in Organic Table Grape Production in Turkey
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. ZEKİ BAYRAMOĞLU →
Energy Use and Carbon Emissions of Walnut Production in Türkiye
2024 ISSN: 2948-2623 SCI-Expanded
Prof. Dr. ZEKİ BAYRAMOĞLU →
Energy Use and Carbon Emissions of Walnut Production in Türkiye
2024 ISSN: 2948-2623 SCI-Expanded Q4
Prof. Dr. HASAN GÖKHAN DOĞAN →
Determination of Energy Use Efficiency and Greenhouse Gas (GHG) Emissions of Pecan (Carya illinoiensis) Production in Türkiye
2024 ISSN: 2948-2623 SCI-Expanded Q3
Dr. Öğr. Üyesi İREM AYRAN ÇOLAK →
Determination of Nutritional Element Contents of Almonds and Walnuts With and Without K-Humate Application
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. MUSTAFA HARMANKAYA →
The Impact of Fruit Production and Trade on Global Climate Change: The Case of EU-27 Countries
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. HASAN GÖKHAN DOĞAN →
Effects of Rhizobacteria on the Nutritional Status of Blackberry Cultivars Grown in Calcareous Soil Conditions
2024 ISSN: 2948-2623 SCI-Expanded
Prof. Dr. MUZAFFER İPEK →
The Relationship Between High Lime Content, Rhizobacteria, and Antioxidant Enzymes in Blackberry Cultivation
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. MUZAFFER İPEK →
Regulation of the Negative Effects of Lime-Induced Stress on Plant Hormone Balance in Blackberry by Plant Growth-Promoting Rhizobacteria
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. MUZAFFER İPEK →
Regulation of the Negative Effects of Lime-Induced Stress on Plant Hormone Balance in Blackberry by Plant Growth-Promoting Rhizobacteria
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. AHMET EŞİTKEN →
The Relationship Between High Lime Content, Rhizobacteria, and Antioxidant Enzymes in Blackberry Cultivation
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. AHMET EŞİTKEN →
Effects of Rhizobacteria on the Nutritional Status of Blackberry Cultivars Grown in Calcareous Soil Conditions
2024 ISSN: 2948-2623 SCI-Expanded Q3
Prof. Dr. AHMET EŞİTKEN →
Strawberries from Konya in the Central Anatolia Region of Türkiye: Phenolic Profile, Antioxidant Potential and Mineral Composition
2024 ISSN: 2948-2623 SCI-Expanded
Doç. Dr. SALİHA DİNÇ →
Effects of Rhizobacteria on the Nutritional Status of Blackberry Cultivars Grown in Calcareous Soil Conditions
2024 ISSN: 2948-2623 SCI-Expanded Q3
Doç. Dr. ŞEYMA ARIKAN →
The Relationship Between High Lime Content, Rhizobacteria, and Antioxidant Enzymes in Blackberry Cultivation
2024 ISSN: 2948-2623 SCI-Expanded Q3
Doç. Dr. ŞEYMA ARIKAN →
Makale Bilgileri
Dergi
Applied Fruit Science
ISSN29482623
Yayın TarihiAralık 2024
Cilt / Sayfa66 · 2123-2133
Scopus ID2-s2.0-85205598398
Özet
The use of artificial intelligence and machine learning algorithms for assessment of apple quality was evaluated in this study. Apples are renowned for containing a variety of nutritional elements. By analyzing apple characteristics, the study aimed to categorize apple quality, thus promoting apple consumption and production. The dataset used consists of 4000 data and eight features provided by an American agricultural company. There were two quality classes of apples: there were 2004 quality apples and 1996 low-quality apples. Artificial intelligence classification algorithms such as multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), k‑nearest neighbor (k-NN), and decision tree (DT) have were to predict apple quality. The performance of the algorithms was evaluated on their ability to accurately predict the quality level of the apples. According to the results of the study, the MLP algorithm achieved the highest classification success with an accuracy rate of 95.63%. The accuracy values of the other algorithms were SVM with 90.75%, k‑NN with 89.75%, RF with 89.63%, and DT with 81%. Apple quality is not achieved by relying on a single feature, but rather by evaluating all the features affecting the apple together. An ideal level of acidity enriches the flavor and texture of food, whereas excessive acidity leads to a sour taste. Due to this complexity, we classified factors affecting apple quality and examined traits separately.
Yazarlar (6)
1
Talha Alperen Cengel
ORCID: 0009-0005-6196-6487
2
Bunyamin Gencturk
ORCID: 0009-0001-0944-2898
3
Elham Tahsin Yasin
ORCID: 0000-0003-3246-6000
4
Muslume Beyza Yildiz
ORCID: 0009-0002-0231-687X
5
Ilkay Cinar
ORCID: 0000-0003-0611-3316
6
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
Apple dataset
Apple quality
Classification of apples
Machine learning
Quality classification
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Applied Fruit Science
Q2
SJR Skoru0,308
H-Index8
YayıncıSpringer Science and Business Media Deutschland GmbH
ÜlkeGermany
Horticulture (Q2)
Metrikler
4
Atıf
6
Yazar
5
Anahtar Kelime