Zeytin Olgunluk Seviyelerinin Derin Öğrenme ile Tespiti ve Yeni Bir Veri Kümesi

Öz

Zeytin meyvesinin olgunluk seviyesi, zeytinyağı kalitesini doğrudan etkileyen en kritik parametrelerden biridir. Hasat sonrası zeytinlerin doğru ve tutarlı şekilde sınıflandırılması, üretim sürecinde kalite kontrolün sağlanması açısından büyük önem taşımaktadır. Ancak geleneksel yöntemler çoğunlukla operatör deneyimine dayalı olup öznel ve tutarsız sonuçlar üretebilmektedir. Bu çalışmada, zeytin meyvelerinin Yeşil, Ala Yeşil ve Siyah olmak üzere üç farklı olgunluk seviyesine göre otomatik olarak sınıflandırılmasına yönelik derin öğrenme tabanlı bir yaklaşım önerilmiştir. Çalışma kapsamında saha koşullarında elde edilen toplam 656 görüntüden oluşan özgün bir veri seti oluşturulmuştur. Veri seti üzerinde DenseNet121, InceptionV3, VGG16, VGG19, ResNet50 ve ResNet101 olmak üzere farklı transfer öğrenme mimarileri kullanılarak karşılaştırmalı analiz gerçekleştirilmiştir. Elde edilen deneysel sonuçlar, DenseNet121 modelinin %98,48 doğruluk, %98,53 kesinlik, %98,48 duyarlılık ve %98,47 F1-skoru ile en yüksek performansı sağladığını göstermektedir. Sonuçlar, derin öğrenme tabanlı yöntemlerin zeytin olgunluk seviyelerinin otomatik belirlenmesinde yüksek doğruluk ve güvenilirlik sunduğunu ortaya koymaktadır

Anahtar Kelimeler

• Zeytin olgunluğu
• Derin öğrenme
• Transfer öğrenme
• Tarımsal kalite kontrol

Detection of Olive Ripeness Levels Using Deep Learning and a New Dataset

Öz

The ripeness level of the olive fruit is one of the most critical parameters directly affecting olive oil quality. The accurate and consistent classification of olives after harvest is of great importance for ensuring quality control during the production process. However, traditional methods are largely based on operator experience and can produce subjective and inconsistent results. In this study, a transfer learning-based approach is proposed for the automatic classification of olive fruits into three different ripeness levels: Green, Light Green, and Black. A unique dataset consisting of a total of 656 images obtained under field conditions was created for this study. A comparative analysis was conducted using different deep learning architectures—including DenseNet121, InceptionV3, VGG16, VGG19, ResNet50, and ResNet101—on the dataset. The experimental results indicate that the DenseNet121 model achieved the highest performance with 98.48% accuracy, 98.53% precision, 98.48% sensitivity, and an F1-score of 98.47. The results demonstrate that deep learning-based methods offer high accuracy and reliability in the automatic determination of olive ripeness levels.

Anahtar Kelimeler

• Olive ripeness
• Deep learning
• Transfer learning
• Agricultural quality control

Kaynakça

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