Comparative Analysis of CNN-Based Deep Learning Architectures for Automatic Plant Disease Classification

Authors : Sedat Örenç, Emrullah Acar, Mehmet Siraç Özerdem

Pages : 961-970

Doi:10.24012/dumf.1777471

View : 83 | Download : 134

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :Early detection of plant diseases is crucial for ensuring crop health and reducing agricultural losses. Traditional visual inspection presents a key opportunity for enhancement, as its dependence on manual effort naturally limits both its speed and accuracy. To address this challenge, this study conducts a comparative analysis of five convolutional neural network based architectures—DenseNet201, EfficientNetB3, ResNet101, ResNet50, and VGG16—for automatic classification of apple leaf diseases, focusing on healthy, powdery mildew, and rust conditions. A publicly available Kaggle dataset consisting of 1,532 images was augmented to 9,284 samples using techniques such as flipping, brightness adjustment, and rotation. Each model was fine-tuned and evaluated based on accuracy, precision, recall, and F1-score. Among these, EfficientNetB3 and VGG16 demonstrated superior classification performance across all classes, achieving up to 95.00% accuracy with perfect precision and recall (100.00%). These results confirm the effectiveness of transfer learning and data augmentation in enhancing disease detection accuracy, offering a promising foundation for real-time plant health monitoring systems.
Keywords : Görüntü işleme, Bitki hastalığı, Derin öğrenme modelleri, Elma yaprağı sınıflandırması

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