Soil Classification using Deep Learning Techniques

The study of soil categorization is a burgeoning field nowadays. Living animals and plants depend heavily on soil for their survival. This study is to classify the different types of soils like Clay soil, Black soil, Red soil and Alluvial soil by using CNN algorithm. Classifying soil is important in fields like farming, geology, and engineering. It helps us make smart choices about the use of land, which crops to grow, plan construction, and to take care of the environment. CNN are a type of Deep Learning technique which is used for image analysis. The model assigns weights and biases to different parts of an input image, enabling them to differentiate and identify different objects within the image. Along with that other deep learning methods can also be employed to enhance accuracy. The image datasets used in this study were obtained from Kaggle and underwent processing with various algorithms. Afterwards, performance metrics were employed to compare the results. To efficiently classify the images based on their labels, VGG19 is used, which is a subset of CNN. This study enables us to effectively classify soil images. The models are trained on a dataset of soil images that had been labeled with their corresponding soil type. This model achieves an accuracy of 94.87% on the test set. The results of this study show that CNNs can be used for effective soil classification. This could lead to the development of more efficient and cost-effective methods for soil classification.