Deep Learning-Based Soil Type Classification for Precision Agriculture Using EfficientNet-B0

This paper discusses the importance of practical approaches to classifying soil types given the growing interest in precision agriculture. We propose an enhanced deep learning solution for soil type identification using a Convolutional Neural Network (CNN) architecture, EfficientNet-B0, to improve agricultural practices. The proposed method involves feature extraction from input image data using deep learning techniques to classify soil types. We incorporate the EfficientNet-B0 model to classify images representing four soil types—Alluvial, Black, Clay, and Red—using a comprehensive dataset. The model achieves 99.6% training accuracy and an impressive 99% test accuracy, demonstrating its ability to differentiate between various soil types. Further model assessment includes comprehensive metrics, such as confusion matrices, classification reports, and additional visualisation tools like cumulative gains and lift charts. Our findings suggest that the EfficientNet-B0 model is not only suitable for classification but also provides valuable interpretive tools, including feature embeddings and statistical measures. These results suggest that this research has the potential to enhance precision agriculture by effectively classifying soil types, thereby improving soil management and agricultural productivity.