Evaluation of CNN, alexnet and GoogleNet for fruit recognition
Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruit...
| id |
Muhammad N.A.; Nasir A.A.; Ibrahim Z.; Sabri N. |
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Muhammad N.A.; Nasir A.A.; Ibrahim Z.; Sabri N. 2-s2.0-85051792349 Evaluation of CNN, alexnet and GoogleNet for fruit recognition 2018 Indonesian Journal of Electrical Engineering and Computer Science 12 2 10.11591/ijeecs.v12.i2.pp468-475 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051792349&doi=10.11591%2fijeecs.v12.i2.pp468-475&partnerID=40&md5=bc509e6b375cb01b53afe5d72ed80419 Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also lead to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available dataset. The experimental results indicate that all these techniques produce excellent recognition accuracy, but basic CNN achieves the fastest recognition result compared with Alexnet and Googlenet. © 2018 Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 25024752 English Article All Open Access; Green Open Access; Hybrid Gold Open Access |
| author |
2-s2.0-85051792349 |
| spellingShingle |
2-s2.0-85051792349 Evaluation of CNN, alexnet and GoogleNet for fruit recognition |
| author_facet |
2-s2.0-85051792349 |
| author_sort |
2-s2.0-85051792349 |
| title |
Evaluation of CNN, alexnet and GoogleNet for fruit recognition |
| title_short |
Evaluation of CNN, alexnet and GoogleNet for fruit recognition |
| title_full |
Evaluation of CNN, alexnet and GoogleNet for fruit recognition |
| title_fullStr |
Evaluation of CNN, alexnet and GoogleNet for fruit recognition |
| title_full_unstemmed |
Evaluation of CNN, alexnet and GoogleNet for fruit recognition |
| title_sort |
Evaluation of CNN, alexnet and GoogleNet for fruit recognition |
| publishDate |
2018 |
| container_title |
Indonesian Journal of Electrical Engineering and Computer Science |
| container_volume |
12 |
| container_issue |
2 |
| doi_str_mv |
10.11591/ijeecs.v12.i2.pp468-475 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051792349&doi=10.11591%2fijeecs.v12.i2.pp468-475&partnerID=40&md5=bc509e6b375cb01b53afe5d72ed80419 |
| description |
Fruit recognition is useful for automatic fruit harvesting. Fruit recognition application can reduce or minimize human intervention during fruit harvesting operation. However, in computer vision, fruit recognition is very challenging because of similar shapes, colors and textures among various fruits. Illuminations changes due to weather condition also lead to a challenging task for fruit recognition. Thus, this paper tends to investigate the performance of basic Convolutional Neural Network (CNN), Alexnet and Googlenet in recognizing nine different types of fruits from a publicly available dataset. The experimental results indicate that all these techniques produce excellent recognition accuracy, but basic CNN achieves the fastest recognition result compared with Alexnet and Googlenet. © 2018 Institute of Advanced Engineering and Science. All rights reserved. |
| publisher |
Institute of Advanced Engineering and Science |
| issn |
25024752 |
| language |
English |
| format |
Article |
| accesstype |
All Open Access; Green Open Access; Hybrid Gold Open Access |
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1828987877623595008 |