Hybrid Approach to Soil Classification: Feature Extraction with VGG16 and SVM

This research presents an efficient soil classification approach using a hybrid model that combines the VGG16 deep learning architecture with a Support Vector Machine (SVM) classifier. The VGG16 model, pre-trained on the ImageNet dataset, is utilized for feature extraction, while the SVM classifier is employed for final classification of soil types. The dataset consists of labelled soil images categorized into different types, such as clay, alluvial, black, and red soils. To enhance model generalization and avoid overfitting, data augmentation techniques were applied during preprocessing. The hybrid model is evaluated on training and validation datasets, demonstrating strong performance with validation accuracy peaking at 97.73%. The results indicate that the VGG16+SVM model effectively leverages deep learning for feature extraction and machine learning for classification, offering a robust solution for soil classification tasks. This approach can be extended to other classification problems, providing potential applications in agriculture and environmental monitoring. Future work may explore further enhancements by fine-tuning the SVM classifier or employing advanced feature extraction methods.