CITRUS DISEASE CLASSIFICATION WITH TRANSFER LEARNING AND CNN BASED MODELS

Halit Çetiner

doi:10.17780/ksujes.1170947

EN TR

CITRUS DISEASE CLASSIFICATION WITH TRANSFER LEARNING AND CNN BASED MODELS

Abstract

In recent years, image processing and deep learning have been widely used in the detection and classification of plant diseases. These uses offer great opportunities for the early detection of plant diseases in agriculture. Early detection of the disease is essential to prevent disease symptoms from spreading to intact leaves and to reduce crop damage. For the stated reasons, a deep learning model with three different approaches has been proposed and used for the classification of diseases that are most common in citrus leaves and affect citrus export to a great extent. Training and test data used in the proposed model are separated according to the K-fold 5 value. For this reason, the average of the performance values obtained according to the K-fold 5 value is presented in the study. As a result of the experimental studies, with the fine-tuned DenseNet201 model, which is the first model, an accuracy rate of 0.95 was achieved. In the second model, with the proposed 21-layer CNN model, an accuracy rate of 0.99 was achieved. The third model is defined to show the progress of the proposed DenseNet201 model over the basic DenseNet201 model. With the CNN method recommended for the classification of citrus grades, Blackspot (citrus black spot (CBS), canker (citrus bacterial cancer (CBC)), greening (huanglongbing (HLB)), and (healthy) Healthy) 100%, 100%, 98% and 100% rates have been reached.

Keywords

TRANSFER ÖĞRENME VE CNN TABANLI MODELLER İLE NARENCİYE HASTALIĞI SINIFLANDIRMASI

Öz

Son yıllarda görüntü işleme ve derin öğrenme bitki hastalıklarının tespiti ve sınıflandırılmasında yaygın olarak kullanılmaktadır. Bu kullanımlar, tarım alanında bitki hastalıklarının erken tespiti için büyük fırsatlar sunmaktadır. Hastalığın erken tespiti, hastalık belirtilerinin sağlam yapraklara yayılmasını engellemek ve mahsule zarar vermesini azaltabilmek için gereklidir. Belirtilen sebeplerden dolayı narenciye yapraklarında en sık görülen ve narenciye ihracatını büyük ölçüde etkileyen hastalıkların sınıflandırılması için üç farklı yaklaşımla derin öğrenme modeli önerilmiş ve kullanılmıştır. Önerilen modellerde kullanılan eğitim ve test verileri K-fold 5 değerine göre ayrılmıştır. Bu nedenle çalışmada K-fold 5 değerine göre elde edilen performans değerlerinin ortalaması sunulmuştur. Deneysel çalışmalar neticesinde birinci model olan ince ayarlı DenseNet201 modeli kullanarak 0.95 doğruluk oranına ulaşılmıştır. İkinci modelde ise önerilen 21 katmanlı CNN modeli ile 0.99 doğruluk oranına ulaşılmıştır. Üçüncü model ise önerilen DenseNet201 modelinin temel DenseNet201 modeline göre ilerlemesini göstermek için tanımlanmıştır. Önerilen CNN yöntemi ile Blackspot (citrius siyah nokta (CBS)), canker (citrius bakteriyel kanseri (CBC)), greening (huanglongbing (HLB)) ve (sağlıklı) Healthy adlı sınıflara sahip olan narenciye bitkisine ait görüntüler sırasıyla %100, %100, %98 ve %100 sınıflandırma oranlarına ulaşılmıştır.

Anahtar Kelimeler

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

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

Publication Date

March 15, 2023

Submission Date

September 5, 2022

Acceptance Date

October 4, 2022

Published in Issue

Year 2023 Volume: 26 Number: 1

DOI

https://doi.org/10.17780/ksujes.1170947

IZ

https://izlik.org/JA52EH25DU

Cite

RIS / Bibtex

APA

Çetiner, H. (2023). CITRUS DISEASE CLASSIFICATION WITH TRANSFER LEARNING AND CNN BASED MODELS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(1), 43-56. https://doi.org/10.17780/ksujes.1170947

CITRUS DISEASE CLASSIFICATION WITH TRANSFER LEARNING AND CNN BASED MODELS

CITRUS DISEASE CLASSIFICATION WITH TRANSFER LEARNING AND CNN BASED MODELS

Abstract

Keywords

TRANSFER ÖĞRENME VE CNN TABANLI MODELLER İLE NARENCİYE HASTALIĞI SINIFLANDIRMASI

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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