Comparative Analysis of Hand-Crafted and Deep Learning Methods for Grape Leaf Disease Classification

Abstract

This study investigates grape leaf disease classification using both hand-crafted feature extraction methods and deep learning models. Principal Component Analysis (PCA) and Local Binary Patterns (LBP) are employed as hand-crafted approaches, while Convolutional Neural Networks, including VGG16, ResNet-50, AlexNet, GoogLeNet, MobileNetV2, and EfficientNet-B0, are utilized as deep learning methods. Experiments are conducted on two publicly available datasets, Grape400 and PlantVillage. The results demonstrate that deep learning models consistently outperform hand-crafted methods across both datasets. VGG16 achieves the highest accuracy of 98.75% on Grape400 and 99.51% on PlantVillage, while MobileNetV2 and EfficientNet-B0 also show competitive performance with accuracies exceeding 97%. In contrast, PCA and LBP yield notably lower accuracies, particularly on the Grape400 dataset. These findings confirm the effectiveness of transfer learning-based deep learning architectures for robust and accurate grape leaf disease classification, highlighting their potential application in precision agriculture.

Keywords

• Grape leaf disease
• Classification
• Deep learning
• Hand-crafted features
• Machine learning

Project Number

Not available.

Ethical Statement

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.

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