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

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.