Domates Yapraklarında Hastalık Tespiti İçin Transfer Öğrenme Metotlarının Kullanılması

KILIÇARSLAN, Serhat; PACAL, Ishak

doi:10.46387/bjesr.1273729

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…The EfficientNetv2_Small model outperformed the others with a test accuracy of 98.01% and an F1-score of 97.99%. In another study, Kılıçarslan and Pacal [20] used DenseNet, ResNet50, and MobileNet architectures to detect diseases in tomato leaves. DenseNet provided the best performance with an accuracy of 99%.…”

Section: Introductionmentioning

confidence: 99%

Data-Efficient Vision Transformer Models for Robust Classification of Sugarcane

Paçal,

Kunduracıoğlu

2024

J. Soft. Comput. Decis. Anal.

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Sugar cane is an important agricultural product that provides 75% of the world's sugar production. As with all plant species, any disease affecting sugarcane can significantly impact yields and planning. Diagnosing diseases in sugarcane leaves using traditional methods is slow, inefficient, and often lacking in accuracy. This study presents a deep learning-based approach for accurate diagnosis of diseases in sugarcane leaves. Specifically, training and evaluation were conducted on the publicly available Sugarcane Leaf Dataset using leading ViT (Vision Transformer) architectures such as DeiT3-Small and DeiT-Tiny. This dataset includes 11 different disease classes and a total of 6748 images. Additionally, these models were compared with popular CNN models. The findings of the study show that there is no direct relationship between model complexity, depth, and accuracy for the 11-class sugarcane dataset. Among the 12 models tested, the DeiT3-Small model showed the highest performance with 93.79% accuracy, 91.27% precision, and 90.96% F1-score. These results highlight that rapid, accurate, and automatic disease diagnosis systems developed using deep learning techniques can significantly improve sugarcane disease management and contribute to increased yields.

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Section: Introductionmentioning

confidence: 99%

Data-Efficient Vision Transformer Models for Robust Classification of Sugarcane

Paçal,

Kunduracıoğlu

2024

J. Soft. Comput. Decis. Anal.

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Detection of Faults in High Voltage Power Transmission Lines Using Unmanned Aerial Vehicle with Artificial Intelligence Methods

Aksoy,

Eylence,

Özmen

2024

Engineering Cyber-Physical Systems and Critical Infrastructures

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Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases

Kunduracioglu,

Pacal

2024

J Plant Dis Prot

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Plant diseases cause significant agricultural losses, demanding accurate detection methods. Traditional approaches relying on expert knowledge may be biased, but advancements in computing, particularly deep learning, offer non-experts effective tools. This study focuses on fine-tuning cutting-edge pre-trained CNN and vision transformer models to classify grape leaves and diagnose grape leaf diseases through digital images. Our research examined a PlantVillage dataset, which comprises 4062 leaf images distributed across four categories. Additionally, we utilized the Grapevine dataset, consisting of 500 leaf images. This dataset is organized into five distinct groups, with each group containing 100 images corresponding to one of the five grape types. The PlantVillage dataset focuses on four classes related to grape diseases, namely Black Rot, Leaf Blight, Healthy, and Esca leaves. On the other hand, the Grapevine dataset includes five classes for leaf recognition, specifically Ak, Alaidris, Buzgulu, Dimnit, and Nazli. In experiments with 14 CNN and 17 vision transformer models, deep learning demonstrated high accuracy in distinguishing grape diseases and recognizing leaves. Notably, four models achieved 100% accuracy on PlantVillage and Grapevine datasets, with Swinv2-Base standing out. This approach holds promise for enhancing crop productivity through early disease detection and providing insights into grape variety characterization in agriculture.

show abstract

Domates Yapraklarında Hastalık Tespiti İçin Transfer Öğrenme Metotlarının Kullanılması

Cited by 4 publications

References 36 publications

Data-Efficient Vision Transformer Models for Robust Classification of Sugarcane

Data-Efficient Vision Transformer Models for Robust Classification of Sugarcane

Detection of Faults in High Voltage Power Transmission Lines Using Unmanned Aerial Vehicle with Artificial Intelligence Methods

Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases

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