A Comparative Analysis of Deep Learning Parameters for Enhanced Detection of Yellow Rust in Wheat

Kemal Adem; Esra Kavalcı Yılmaz; Fatih Ölmez; Kübra Çelik; Halit Bakır

Makale detayları

A Comparative Analysis of Deep Learning Parameters for Enhanced Detection of Yellow Rust in Wheat

Yazarlar:

Kemal Adem | Esra Kavalcı Yılmaz | Fatih Ölmez | Kübra Çelik | Halit Bakır

Yayıncı:

Kirikkale University

Ana Başlıklar:

Decision Support and Group Support Systems

Dergi:

International Journal of Engineering Research and Development Vol.16 Issue 2, pp.659-667

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314836

pISSN:

1308-5506

Anahtar kelimeler:

Özet:

 Wheat, one of the most important food sources in human history, is one of the most important cereal crops produced and consumed in our country. However, if diseases such as yellowpas, which is one of the risk factors in wheat production, cannot be detected in a timely and accurate manner, situations such as decreased production may be encountered. For this reason, it is more advantageous to use decision support systems based on deep learning in the detection and classification of diseases in agricultural products instead of experts who perform the processes in a longer time and have a higher error rate. In this study, the effects of the number of layers, activation function and optimization algorithm variables on the classification of deep learning models used for the classification of yellow rust disease in wheat were examined. As a result of the study, the highest success value was obtained with 97.36 accuracy when using a 5-layer CNN model using Leaky ReLU activation function and Nadam optimization algorithm.

 Wheat, one of the most important food sources in human history, is one of the most important cereal crops produced and consumed in our country. However, if diseases such as yellowpas, which is one of the risk factors in wheat production, cannot be detected in a timely and accurate manner, situations such as decreased production may be encountered. For this reason, it is more advantageous to use decision support systems based on deep learning in the detection and classification of diseases in agricultural products instead of experts who perform the processes in a longer time and have a higher error rate. In this study, the effects of the number of layers, activation function and optimization algorithm variables on the classification of deep learning models used for the classification of yellow rust disease in wheat were examined. As a result of the study, the highest success value was obtained with 97.36 accuracy when using a 5-layer CNN model using Leaky ReLU activation function and Nadam optimization algorithm.

 Wheat, one of the most important food sources in human history, is one of the most important cereal crops produced and consumed in our country. However, if diseases such as yellowpas, which is one of the risk factors in wheat production, cannot be detected in a timely and accurate manner, situations such as decreased production may be encountered. For this reason, it is more advantageous to use decision support systems based on deep learning in the detection and classification of diseases in agricultural products instead of experts who perform the processes in a longer time and have a higher error rate. In this study, the effects of the number of layers, activation function and optimization algorithm variables on the classification of deep learning models used for the classification of yellow rust disease in wheat were examined. As a result of the study, the highest success value was obtained with 97.36 accuracy when using a 5-layer CNN model using Leaky ReLU activation function and Nadam optimization algorithm.

 İnsanlık tarihinin en önemli besin kaynaklarından biri olan buğday ülkemizde de üretimi ve tüketimi yapılmakta olan en önemi tahıl ürünlerinden biridir. Fakat buğday üretimindeki risk faktörlerinden biri olan sarıpas gibi hastalıkların zamanında ve doğru bir şekilde tespit edilememesi durumunda üretimin azalması gibi durumlarla karşı karşıya kalınabilmektedir. Bu nedenle zirai ürünlerdeki hastalıkların tespit ve sınıflandırılması işlemlerinde daha uzun sürede işlemleri gerçekleştiren ve hata yapma oranı daha yüksek olan uzmanlar yerine derin öğrenmeye dayalı karar destek sistemlerinin kullanılması daha avantajlıdır. Bu çalışmada da buğdaydaki sarı pas hastalığının sınıflandırılması işlemleri için kullanılan derin öğrenme modellerinde katman sayısı, aktivasyon fonksiyonu ve optimizasyon algoritması değişkenlerinin sınıflandırmaya etkisi incelenmiştir. Çalışma sonucunda en yüksek başarı değeri 97,36 doğruluk ile Leaky ReLU aktivasyon fonksiyonu ve Nadam optimizasyon algoritması kullanan 5 katmanlı CNN modeli kullanıldığında elde edilmiştir.

Makale detayları

A Comparative Analysis of Deep Learning Parameters for Enhanced Detection of Yellow Rust in Wheat

Yazarlar:

Kemal Adem | Esra Kavalcı Yılmaz | Fatih Ölmez | Kübra Çelik | Halit Bakır

Yayıncı:

Kirikkale University

Ana Başlıklar:

Decision Support and Group Support Systems

Dergi:

International Journal of Engineering Research and Development Vol.16 Issue 2, pp.659-667

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314836

pISSN:

1308-5506

“Wheat

Yellow Rust

Deep learning

Activation function

Optimizer”Buğday

Sarı Pas

Derin Öğrenme

Aktivasyon Fonksiyonu

Optimizasyon

Sayı

Makale detayları

Yazarlar: Kemal Adem | Esra Kavalcı Yılmaz | Fatih Ölmez | Kübra Çelik | Halit Bakır

Yayıncı: Kirikkale University

Ana Başlıklar: Decision Support and Group Support Systems

Dergi: International Journal of Engineering Research and Development Vol.16 Issue 2, pp.659-667

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001314836

pISSN: 1308-5506

Sayı

Yazarlar:

Kemal Adem | Esra Kavalcı Yılmaz | Fatih Ölmez | Kübra Çelik | Halit Bakır

Yayıncı:

Kirikkale University

Ana Başlıklar:

Decision Support and Group Support Systems

Dergi:

International Journal of Engineering Research and Development Vol.16 Issue 2, pp.659-667

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314836

pISSN:

1308-5506