Derin Evrişimli Sinir Ağları Kullanılarak Bazı Ayçiçeği Hastalıklarının Belirlenmesi

Abstract

Türkiye’de yetiştirilen yağ bitkileri arasında ayçiçeği ekim alanı ve verim açısından ilk sırada yer almaktadır. Dolayısıyla ayçiçek hastalıklarının hızlı tespiti üeticilerin bu kısa sürede önlem almalarına yarayacaktır. Bilgisayar tabanlı derin öğrenme teknikleri bu hastalıkların yüksek doğruluk ile tahmin edilebilmesini mümkün kılmıştır. Bu çalışmada 3 farklı ayçiçek hastalığının tespitinde ücretsiz bir bulut tabanlı Python kodlama ortamı olan Google Colaboratory (GC) kullanılmıştır. Toplamda 760 görüntü Edirne ili İpsala ilçesinde 2022-2023 üretim sezonlarında elde edilerek incelenmiştir. Geliştirilen Convolutional Neural Network (CNN) modeline bir dizi veri ön işleme teknikleri uygulanmış ve 3 farklı ayçiçek hastalığı tahmin sistemi yaratılmıştır. Modelin 0.90 doğrulukla sınıflandırma yapabildiği ortaya konmuştur.

Keywords

• Görüntü sınıflandırma
• Derin öğrenme
• Makine öğrenmesi
• Ayçiçek hastalıkları

Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks

Abstract

Among the oilseed plants cultivated in Türkiye, sunflower ranks first in terms of cultivation area and production. Therefore, short time detection of sunflower diseases will help producers to take necessary actions on time. Computer-based deep learning techniques have made it possible to predict these diseases with high accuracy. In this study, Google Collaboratory (GC), a free cloud-based Python coding environment, was used to detect 3 different sunflower diseases. A total of 760 images were obtained and examined in the 2022-2023 production seasons in İpsala district of Edirne province. A series of data pre-processing techniques were applied to the developed Convolutional Neural Network (CNN) model and 3 different sunflower disease prediction systems were created. It has been revealed that the model can classify with an accuracy of 0.90.

Keywords

• Image classification
• Deep learning
• Machine learning
• Sunflower diseases

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