Araştırma Makalesi

Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks

Cilt: 12 Sayı: 1 22 Temmuz 2024
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Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks

Öz

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.

Anahtar Kelimeler

Kaynakça

  1. Altınbilek, H.F., Kızıl, Ü., 2022. Identification of some paddy rice diseases using deep convolutional neural networks. Yuzuncu Yil University Journal of Agricultural Sciences. 32(4): 705-713.
  2. Aslan, M., 2022. CoviDetNet: A new -19 diagnostic system based on deep features of chest x-ray. International Journal of Imaging Systems and Technology. 32(5): 1447-1463.
  3. Camargo, A., Smith, J.S., 2009. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering. 102: 9–21.
  4. Dawod, R.G., Dobre, C., 2022. Automatic segmentation and classification system for foliar diseases in sunflower. Sustainability. 14: 11312.
  5. Deb, D., Khan, A., Dey, N., 2020. Phoma diseases: Epidemiology and control. Plant Pathology. 69: 1203–1217.
  6. Demir, F., Türkoglu, M., Aslan, M., Şengür, A., 2020. A new pyramidal concatenated cnn approach for environmental sound classification. Applied Acoustics. 170: 107520.
  7. Devaraj, A., Rathan, K., Jaahnavi, S., Indira, K., 2019. Identification of plant disease using image processing technique. International Conference on Communication and Signal Processing. 4-6 April 2019, Chennai, India.
  8. Ensari, T., Armah, D.C., Balsever, A.E., Dağtekin, M., 2020. Görüntü tabanlı dijital bitki fenotiplemesi için konvolüsyonel sinir ağları. European Journal of Science and Technology. (Special Issue): 338-342.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Ekolojik Uygulamalar (Diğer)

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

22 Temmuz 2024

Gönderilme Tarihi

7 Kasım 2023

Kabul Tarihi

29 Şubat 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 12 Sayı: 1

Kaynak Göster

APA
Altınbılek, H. F., & Kızıl, Ü. (2024). Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks. ÇOMÜ Ziraat Fakültesi Dergisi, 12(1), 11-19. https://doi.org/10.33202/comuagri.1387580
AMA
1.Altınbılek HF, Kızıl Ü. Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks. ÇOMÜ Ziraat Fakültesi Dergisi. 2024;12(1):11-19. doi:10.33202/comuagri.1387580
Chicago
Altınbılek, Hakkı Fırat, ve Ünal Kızıl. 2024. “Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks”. ÇOMÜ Ziraat Fakültesi Dergisi 12 (1): 11-19. https://doi.org/10.33202/comuagri.1387580.
EndNote
Altınbılek HF, Kızıl Ü (01 Temmuz 2024) Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks. ÇOMÜ Ziraat Fakültesi Dergisi 12 1 11–19.
IEEE
[1]H. F. Altınbılek ve Ü. Kızıl, “Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks”, ÇOMÜ Ziraat Fakültesi Dergisi, c. 12, sy 1, ss. 11–19, Tem. 2024, doi: 10.33202/comuagri.1387580.
ISNAD
Altınbılek, Hakkı Fırat - Kızıl, Ünal. “Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks”. ÇOMÜ Ziraat Fakültesi Dergisi 12/1 (01 Temmuz 2024): 11-19. https://doi.org/10.33202/comuagri.1387580.
JAMA
1.Altınbılek HF, Kızıl Ü. Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks. ÇOMÜ Ziraat Fakültesi Dergisi. 2024;12:11–19.
MLA
Altınbılek, Hakkı Fırat, ve Ünal Kızıl. “Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks”. ÇOMÜ Ziraat Fakültesi Dergisi, c. 12, sy 1, Temmuz 2024, ss. 11-19, doi:10.33202/comuagri.1387580.
Vancouver
1.Hakkı Fırat Altınbılek, Ünal Kızıl. Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks. ÇOMÜ Ziraat Fakültesi Dergisi. 01 Temmuz 2024;12(1):11-9. doi:10.33202/comuagri.1387580

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