Large Scale Image Classification of Exotic Fruits in Indonesia Using Transfer Learning Method with MobileNet Model
- DOI
- 10.2991/978-94-6463-140-1_68How to use a DOI?
- Keywords
- CNN; exotic fruit; image classification; transfer learning
- Abstract
Exotic fruit is a fruit that is not widely known to the public. In Indonesia, there are many exotic fruits such as rambutan, passion fruit, mangosteen, longan, guava, and many more. Classification of exotic fruit images is needed because of the lack of knowledge from outsiders about exotic fruits in Indonesia. To his end, developing robust artificial intelligence using deep learning is necessary. CNN is the development of the Multilayer Perceptron (MLP) which is designed to process two-dimensional data and in the type of Deep Neural Network because of the high network depth and widely applied to image data. By utilizing the transfer learning method and a little fine-tuning, the efficient model like MobileNet expected to be better than without transfer learning in FruitNet model. Our contribution is applying efficient transfer learning MobileNet for Exotix Fruits in Indonesia which achieves 87% accuracy in average using more than 1000 images. The model performs better than previous model of FruitNet which only reaches 43% accuracy in average.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Asyora Dewi Prabandani AU - Novanto Yudistira AU - Ayu Raisa Khairun Nisa PY - 2023 DA - 2023/04/30 TI - Large Scale Image Classification of Exotic Fruits in Indonesia Using Transfer Learning Method with MobileNet Model BT - Proceedings of the 2022 Brawijaya International Conference (BIC 2022) PB - Atlantis Press SP - 675 EP - 685 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-140-1_68 DO - 10.2991/978-94-6463-140-1_68 ID - Prabandani2023 ER -