YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ

Halit Çetiner

doi:10.54365/adyumbd.988049

EN TR

PRE-TRAINED NETWORK BASED DEEP NETWORK MODEL FOR CLASSIFICATION OF LEAF DISEASES

Abstract

Early diagnosis of plant-damaging diseases is very important to reduce the consumption of chemical pesticides, to save money and to prevent pollution to the environment. In case of any disease in apple tree leaves, farmers have to get support from expert agricultural personnel in order to detect the signs of disease at an early stage. This situation creates a great cost to the farmers. In order to solve the aforementioned problem, a deep learning model based on the Convolutional Neural Network (CNN) method has been developed to classify scab, rust and multiple disease groups in which both can be used together. The proposed approach consists of a combination of CNN layers using popular transfer learning techniques DenseNet201, MobileNetV2, ResNet50V2, ResNet101V2, ResNet152V2 algorithms as input layer. The developed method has been tested on a data set with high difficulty level, which includes different levels of illumination, noise, and heterogeneous illumination. With the CNN-based method recommended in the test processes, the classification accuracy rate was 97%.

Keywords

CNN , apple leaf disease , classification , transfer learning

YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ

Öz

Bitkiye zarar veren hastalıkların erken teşhisi, kimyasal tarım ilaçlarının tüketimini azaltmak, mali olarak tasarruf etmek ve çevreye verilen kirliliği engelleyebilmek için oldukça önemlidir. Elma ağaç yapraklarında oluşan herhangi bir hastalık durumunda, hastalık belirtilerini erken aşamada tespit edebilmek için çiftçiler uzman tarım personelinden destek almak zorunda kalmaktadır. Bu durum çiftçilere büyük bir maliyet oluşturmaktadır. Bahsedilen problemi çözebilmek adına scab, rust ve her ikisinin bir arada kullanılabileceği çoklu hastalık gruplarını sınıflandırabilmek için Konvolüsyonel Sinir Ağı (CNN) yöntemi tabanlı derin öğrenme modeli geliştirilmiştir. Önerilen yaklaşım popüler transfer öğrenim teknikleri olen DenseNet201, MobileNetV2, ResNet50V2, ResNet101V2, ResNet152V2 algoritmalarını giriş katmanı olarak kullanan CNN katmanlarının birleşiminden oluşmaktadır. Geliştirilen yöntem farklı seviyelerde aydınlatma, gürültü, arka planı homojen olmama durumlarını içeren zorluk seviyesi yüksek bir veri seti üzerinde test edilmiştir. Test işlemlerinde önerilen CNN tabanlı yöntemle sınıflandırma doğruluk oranı olarak %97 değerine ulaşılmıştır.

Anahtar Kelimeler

CNN , elma yaprak hastalığı , sınıflandırma , transfer öğrenme

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Details

Primary Language

Turkish

Subjects

Engineering

Journal Section

Research Article

Authors

Halit Çetiner ^*
0000-0001-7794-2555
Türkiye

Publication Date

December 31, 2021

Submission Date

August 28, 2021

Acceptance Date

September 30, 2021

Published in Issue

Year 2021 Volume: 8 Number: 15

DOI

https://doi.org/10.54365/adyumbd.988049

IZ

https://izlik.org/JA52CS82DP

APA

Çetiner, H. (2021). YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 8(15), 442-456. https://doi.org/10.54365/adyumbd.988049

AMA

1.Çetiner H. YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2021;8(15):442-456. doi:10.54365/adyumbd.988049

Chicago

Çetiner, Halit. 2021. “YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 8 (15): 442-56. https://doi.org/10.54365/adyumbd.988049.

EndNote

Çetiner H (December 1, 2021) YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 8 15 442–456.

IEEE

[1]H. Çetiner, “YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 8, no. 15, pp. 442–456, Dec. 2021, doi: 10.54365/adyumbd.988049.

ISNAD

Çetiner, Halit. “YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 8/15 (December 1, 2021): 442-456. https://doi.org/10.54365/adyumbd.988049.

JAMA

1.Çetiner H. YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2021;8:442–456.

MLA

Çetiner, Halit. “YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 8, no. 15, Dec. 2021, pp. 442-56, doi:10.54365/adyumbd.988049.

Vancouver

1.Halit Çetiner. YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2021 Dec. 1;8(15):442-56. doi:10.54365/adyumbd.988049

YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ

PRE-TRAINED NETWORK BASED DEEP NETWORK MODEL FOR CLASSIFICATION OF LEAF DISEASES

Abstract

Keywords

YAPRAK HASTALIKLARININ SINIFLANDIRILABİLMESİ İÇİN ÖNCEDEN EĞİTİLMİŞ AĞ TABANLI DERİN AĞ MODELİ

Öz

Anahtar Kelimeler

References

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