Avocado Fruit Image Classification Using Resnet Convolutional Neural Networks

Abstract

Deep artificial neural networks have become a popular study topic in the machine learning and pattern detection communities in current history. As living beings, we tend to categorize things, something that will just come back are often categorized into a class or classes. within the business, it is a routine troublesome like, categorizing into components, assemblies, fixtures, and merchandise which is a component of the routine. It is often the rationale why living beings started off with algorithms like Machine Learning (ML), Neural Networks (NN), and Deep Learning (DL), among different techniques to alter the method of categorization. In the proposed work we try to implement architectures, ResNet using Keras-Tensorflow library. The efficiency statistics are displayed and debated. ResNet is trained over a large Dataset - Imagenet and from there brings the generalization aspect of it. There are several trained layers which we can use in our classification. The recommended work's primary focus is to demonstrate methods to construct a CNN model for picture recognition and classification. A customized CNN is imposed and contrasted to a ResNet CNN for the purposes of this research. © 2023 IEEE.