An efficient IoT-based soil image recognition system using hybrid deep learning for smart geotechnical and geological engineering applications

Soil color recognition through AI approaches is crucial for efficient and rapid soil analysis in geotechnical engineering. It enables improved site characterization and supports informed decision-making in engineering projects. The integration of soil color data with other geospatial information enhances geological mapping and modeling capabilities. The expansion of deep learning can assist different stockholders in soil classification as a significant study in recent years. Therefore, this paper presented an efficient IoT-based hybrid CNN-SVM model to classify five soil classes through soil images. The recommended framework utilizes a hybrid model based on CNN as feature selection. After that, a multiclass SVM as a classifier is utilized for the soil classification task in an effective manner as well as new real-time IoT-based portable soil detection devices for geotechnical engineers in Geo-sites. The proposed framework is estimated using a dataset that comprehends a total of 252 soil images for investigation purposes with different evaluation metrics. The proposed hybrid framework using CNN models such as Squeezenet, Alexnet, and Resnet50 with the multiclass SVM classifier gives good accuracy of 86%, 96%, and 95%, respectively. In contrast, the CNN models only give accuracy of 80%, 89%, and 87% for the Squeezenet, Alexnet, and Resnet50, respectively. From the obtained results, we observed that the offered hybrid Alexnet-SVM gives a higher performance while the Squeezenet-SVM gives the lowest concert. Overall, the attained results showed that the recommended hybrid framework gives the best concert for soil classification, which can help in making an efficient support decision-making system for real-time geotechnical engineering applications.