Defect detection in apple (Granny Smith) with deep learning techniques

Zeynep Ünal; Tefide Kızıldeniz; Mustafa Özden; Hakan Aktaş; Ömer Karagöz

Makale detayları

Defect detection in apple (Granny Smith) with deep learning techniques

Yazarlar:

Zeynep Ünal | Tefide Kızıldeniz | Mustafa Özden | Hakan Aktaş | Ömer Karagöz

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 4, pp.1119-1129

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001301217

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 During apple Malus communis L. harvesting, physical damage that reduces the quality of the product is inevitable. Early detection and separation of damaged fruits is important in terms of increasing their commercial value. Undetected defective products reduce the production volume as well as food loss, since they affect the quality of intact products. The aim of this study is to detect defects in apples using deep learning techniques on images taken from the Granny Smith apple cultivar. A technique that does not require special conditions and that will make classification and defect detection cost-effectively has been researched. In the study, the test accuracy of the InceptionV3 model was 100 after 100 epochs, and the test accuracy of the AlexNet model was 98.33. A method has been developed that can prevent economic losses that may occur after harvesting by detecting and separating the damages that occur on the fruit during harvesting with deep learning techniques.

 During apple Malus communis L. harvesting, physical damage that reduces the quality of the product is inevitable. Early detection and separation of damaged fruits is important in terms of increasing their commercial value. Undetected defective products reduce the production volume as well as food loss, since they affect the quality of intact products. The aim of this study is to detect defects in apples using deep learning techniques on images taken from the Granny Smith apple cultivar. A technique that does not require special conditions and that will make classification and defect detection cost-effectively has been researched. In the study, the test accuracy of the InceptionV3 model was 100 after 100 epochs, and the test accuracy of the AlexNet model was 98.33. A method has been developed that can prevent economic losses that may occur after harvesting by detecting and separating the damages that occur on the fruit during harvesting with deep learning techniques.

 During apple Malus communis L. harvesting, physical damage that reduces the quality of the product is inevitable. Early detection and separation of damaged fruits is important in terms of increasing their commercial value. Undetected defective products reduce the production volume as well as food loss, since they affect the quality of intact products. The aim of this study is to detect defects in apples using deep learning techniques on images taken from the Granny Smith apple cultivar. A technique that does not require special conditions and that will make classification and defect detection cost-effectively has been researched. In the study, the test accuracy of the InceptionV3 model was 100 after 100 epochs, and the test accuracy of the AlexNet model was 98.33. A method has been developed that can prevent economic losses that may occur after harvesting by detecting and separating the damages that occur on the fruit during harvesting with deep learning techniques.

 Elma Malus communis L. derimi sırasında ürünün kalitesini düşüren fiziksel zararlanmaların oluşması kaçınılmazdır. Zarar gören meyvelerin erken tespit edilerek ayrılması ticari değerinin artırılması açısından önemlidir. Tespit edilemeyen kusurlu ürünler sağlam ürünlerin kalitesini etkilediğinden dolayı gıda kaybının yanı sıra üretim hacmini de düşürmektedir. Çalışmanın amacı, Granny Smith elma çeşidinden alınan görüntüler üzerinde, derin öğrenme teknikleri kullanarak elmalarda kusur tespit etmektir. Özel koşul gerektirmeyen, uygun maliyetle sınıflandırma ve kusur tespiti yapacak bir teknik araştırılırmıştır. Çalışmada, InceptionV3 modelinin 100 çevrim sonunda test doğruluğu 100, AlexNet modelinin ise test doğruluğu 98.33 elde edilmiştir. Derin öğrenme teknikleriyle, derim sırasında meyve üzerinde oluşan zararlar tespit edilerek ayrılmasıyla, derim sonrası oluşabilecek ekonomik kayıpların önüne geçebilecek bir yöntem geliştirilmiştir.

Makale detayları

Defect detection in apple (Granny Smith) with deep learning techniques

Yazarlar:

Zeynep Ünal | Tefide Kızıldeniz | Mustafa Özden | Hakan Aktaş | Ömer Karagöz

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 4, pp.1119-1129

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001301217

pISSN:

2564-6605

AlexNet

Apple classification

Bruise detection

Deep learning

Granny SmithAlexNet

Derin öğrenme

Elma sınıflandırması

Granny Smith

InceptionV3

Kusur Tespiti

Sayı

Makale detayları

Yazarlar: Zeynep Ünal | Tefide Kızıldeniz | Mustafa Özden | Hakan Aktaş | Ömer Karagöz

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 4, pp.1119-1129

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001301217

pISSN: 2564-6605

Sayı

Yazarlar:

Zeynep Ünal | Tefide Kızıldeniz | Mustafa Özden | Hakan Aktaş | Ömer Karagöz

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 4, pp.1119-1129

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001301217

pISSN:

2564-6605