Comparative Analysis of Deep Learning Techniques for Soil Image Classification - Chate, Girish D; Bhamare, S.S (International Journal of Computer Sciences and Engineering, 2025)
Soil classification is a crucial step in agricultural and environmental planning. Current innovations in computer vision and deep learning have enabled automatic soil classification using image-based approaches. This paper, explore comparative analysis of two popular convolutional neural network architectures, VGG16 and VGG19, for soil image classification. A use of soil image dataset containing various soil types used to evaluate the performance of both models. These models fine-tuned using transfer learning, and performance determined using metrics such as accuracy, precision, recall, F1-score, and training time. The result shows that both VGG16 and VGG19 achieve high classification accuracy, with VGG19 slightly outperforming than VGG16 in terms of accuracy but requiring more computational resources and time. This paper demonstrates the effectiveness of deep learning models in soil image classification and provides understandings into their comparative performance.