Atıf İçin Kopyala
Gerdan D., Koç C., Vatandaş M.
TARIM BILIMLERI DERGISI, cilt.29, sa.2, ss.627-638, 2023 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
29
Sayı:
2
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Basım Tarihi:
2023
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Doi Numarası:
10.15832/ankutbd.957265
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Dergi Adı:
TARIM BILIMLERI DERGISI
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, TR DİZİN (ULAKBİM)
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Sayfa Sayıları:
ss.627-638
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Ankara Üniversitesi Adresli:
Evet
Özet
Tomato is one of the most important vegetables in the world. Presence of diseases and pests in the growing area significantly affect the choice of variety in tomato. Early-stage diagnosis plays an important role in determining whether the tomato is subject to effective and economical chemical, mechanical and biological controls, and internal and external quarantine. In this study, deep learning was used to diagnose some diseases in tomatoes. For this purpose, a novel deep CNN-based approach and some Keras models including DenseNet201, InceptionResNetV2, MobileNet, VGG16 architectures were used. Early, middle, and late stages of 18.456 images of Bacterial Spot, Early Blight, Leaf Mold, Septoria Leaf Spot, Target Spot, Mosaic Virus, Yellow Leaf Curl Virus and healthy leaves were examined. The experimental results showed that the custom CNN model produced 99.82% training accuracy. We recommend this model in terms of monitoring and diagnosing of tomato diseases. The results obtained with this study can be used in robotic spraying and harvesting operations.