Soil Image Classification using Deep Learning

Image classification is an important topic of study in the field of image processing nowadays and is a popular area of research. By providing the computer with data to learn from, image categorization was created to close the gap between computer vision and human vision. In this paper, the methods for categorising images using traditional machine learning and deep learning are compared and investigated. This study employs a tensor flow framework and convolutional neural networks to classify images. This paper implements CNN in binary classification and multi-class classification for object identification and analyses the performance of well-known convolutional neural networks (CNNs). We built five unique image datasets on our own for multiclass classification, using the dog vs. cat dataset for binary classification. For our investigation, we classified the photos using a separate machine learning model and then classified them again using CNN because evaluating CNN’s performance on a single data set hides its actual potential and limits. Additionally, trained CNNs perform very differently across various categories of objects, and we will thus talk about some potential causes.