Identification of Some Sunflower Diseases Using Deep Convolutional Neural Networks
Öz
Anahtar Kelimeler
Kaynakça
- 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.
- 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.
- Camargo, A., Smith, J.S., 2009. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering. 102: 9–21.
- Dawod, R.G., Dobre, C., 2022. Automatic segmentation and classification system for foliar diseases in sunflower. Sustainability. 14: 11312.
- Deb, D., Khan, A., Dey, N., 2020. Phoma diseases: Epidemiology and control. Plant Pathology. 69: 1203–1217.
- Demir, F., Türkoglu, M., Aslan, M., Şengür, A., 2020. A new pyramidal concatenated cnn approach for environmental sound classification. Applied Acoustics. 170: 107520.
- 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.
- 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
Yazarlar
Ünal Kızıl
0000-0002-8512-3899
Türkiye
Yayımlanma Tarihi
22 Temmuz 2024
Gönderilme Tarihi
7 Kasım 2023
Kabul Tarihi
29 Şubat 2024
Yayımlandığı Sayı
Yıl 2024 Cilt: 12 Sayı: 1
Cited By
An explainable deep learning model for mulberry leaf classification and disease detection
Engineering Applications of Artificial Intelligence
https://doi.org/10.1016/j.engappai.2025.113470