The Classification Of Vegetables and Fruits Using Deep Learning

Abstract

In this paper, an attempt is made to accurately classify vegetables and fruits. This classification uses a dataset of 6000 photos divided into 15 classes. Convolutional neural networks, a type of deep learning algorithm, are the most efficient tool in the machine learning field for classification issues. However, CNN requires huge datasets to perform well in natural image classification tasks. We undertake an experiment on the performance of CNN for vegetables and fruits categorization by building a CNN model from scratch. Furthermore, multiple pre-trained CNN architectures using transfer learning are used to evaluate accuracy to the standard CNN. This paper suggests the study of such standard CNNs and their architectures (VGG16, InceptionV3, ResNet, etc.) to create up which strategy would be most accurate and effective for fresh image datasets. Experimental findings are presented for all proposed CNN designs. Additionally, a comparison is made between constructed CNN models and pre-trained CNN architectures. Furthermore, the paper demonstrates that by leveraging previous information collected from comparable large-scale studies, the transfer learning technique outperforms classical CNN with a small dataset. Another addition to this paper is that we created a dataset of 15 vegetables and fruits groups, total 6000 images. This study examines the use of convolutional neural networks (CNNs) to categorize and assess the quality of fruits and vegetables. A web application using deep learning and computer vision, powered by a Python-based CNN algorithm, marks a significant advancement in combining machine learning with agricultural technology. The application accurately categorizes and grades quality. The study used two datasets: 6000 photos in 15 categories for fruits and vegetable categorization and 6000 images in 15 categories for quality assessment. By using images data, this gadget can determine how much fruit or vegetables and fruits are rotting and how fresh it is. Our aim is to reach all the general people for this reason we also build android platform. Here user click one picture then our system automatically generates and show the result We believe, it will be advantageous for all farmers, business owners, and common people.