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

Year 2024, Volume: 16 Issue: 2, 659 - 667, 30.06.2024

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

https://doi.org/10.29137/umagd.1390763

Cited By: 1

Abstract

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.

Keywords

“Wheat, Yellow Rust, Deep learning, Activation function, Optimizer”

Supporting Institution

Sivas University of Science and Technology

Project Number

2023-GENL-Müh-0003

Thanks

“This work has been supported by the Scientifıc Research Projects Coordination Unit of the Sivas University of Science and Technology. Project Number: 2023-GENL-Müh-0003”

Buğdayda Sarı Pasın Gelişmiş Tespiti için Derin Öğrenme Parametrelerinin Karşılaştırmalı Analizi

Abstract

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.

Keywords

Buğday, Sarı Pas, Derin Öğrenme, Aktivasyon Fonksiyonu, Optimizasyon

Project Number

2023-GENL-Müh-0003

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