Fruit Image Classification Based on SVM, Decision Tree and KNN

Xiang Han

2023

Abstract

Image classification is becoming more and more popular in today’s daily life. Image classification is widely used in many fields. For example, the market demand for face recognition technology has increased significantly in recent years. The foundation of these new technologies is still image classification. In order to explore the efficiency of different image classification algorithms and help guide the use of different image classification algorithms in the market, this paper uses a variety of algorithms to classify images in the fruit360 dataset. Fruit360 dataset is a dataset with 90483 images of 131 kinds of fruits and vegetables. Images in this dataset have a size of 100×100 pixels. As a result, the Support Vector Machine algorithm is 89% accurate, the decision tree algorithm is 94% accurate, and the K-nearest Neighbors algorithm is nearly 100% accurate. Apart from the accuracy of these algorithms, this paper also analyzes the difference in classification accuracy among different classes. For the Support Vector Machine algorithm, the classification accuracy of class 1 and class 2 is low, which is caused by the algorithm itself. For the decision tree algorithm, the accuracy of each classification group is similar. For the K-nearest Neighbors algorithm, the overall accuracy is very high. In addition, this paper also compares the characteristics of these three algorithms, analyzes the performance difference between the Support Vector Machine algorithm and the decision tree algorithm, and discusses the relationship between the Support Vector Machine algorithm efficiency and the number of classes.

Paper Citation

in Harvard Style

Han X. (2023). Fruit Image Classification Based on SVM, Decision Tree and KNN. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 367-373. DOI: 10.5220/0012815800003885

in Bibtex Style

@conference{daml23,
author={Xiang Han},
title={Fruit Image Classification Based on SVM, Decision Tree and KNN},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={367-373},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012815800003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Fruit Image Classification Based on SVM, Decision Tree and KNN
SN - 978-989-758-705-4
AU - Han X.
PY - 2023
SP - 367
EP - 373
DO - 10.5220/0012815800003885
PB - SciTePress