A comprehensive introduction to convolutional neural networks: a case study for leaf image classification

Abstract

Most of the today’s new and innovative artificial intelligence applications are based on the artificial neural types of networks to capture, interpret and analyze various kind of data. A Convolutional Neural Network is a type of artificial neural network used primarily for image recognition and processing due to its ability to patterns in images. A deep learning algorithm is adopted to classify images and detect objects in an image with the neural network. In this study, Convolutional Neural Networks Model is used for performing automatic feature extraction on binary classification with leaf image dataset. It works by investigating and processing large amount of data in a grid format and then extracting important features for classification detection. It is discussed in this study the use of deep learning techniques to automatically detect diseases in plants. Further, it emphasizes the working principles of Convolutional Neural Networks. Hence, the study intends to present a comprehensive way of the modeling techniques. Moreover, the study seeks fundamentals on deep learning mechanisms which is useful to beginners on the area of artificial intelligence. Therefore, this study is carried out intentionally as a case study which emphasizes the automatic leaf image binary classification. The findings of the present study showed 81.3% of the maximum accuracy of the modeling.