CNN Transfer Learning for Automatic Fruit Recognition for Future Class of Fruit
Deep fruit recognition model learned on big dataset outperform fruit recognition task on difficult unconstrained fruit dataset. But in practice, we are often lack of resources to learn such a complex model, or we only have very limited training samples for a specific fruit recognition task. In this study we address the problem of adding new classes to an existing deep convolutional neural network framework. We extended our prior work for automatic fruit recognition by applying transfer learning techniques to adding new classes to existing model which was trained for 15 different kind of fruits. Pre-trained model was previously trained on a large-scale dataset of 44406 images. To add new class of fruit in our pre-trained model, we need to train a new classifier which will be trained for scratch, on the top of pre-trained model so, that we can re- purpose the feature learned previously for the dataset. Transfer learning using our pre-trained model has been demonstrated to give the best classification accuracy of 95.00%. The experimental results demonstrate that our proposed CNN framework is superior to the previous state-of-the- art networks.
. A. Rocha, D. C. Hauagge, J. Wainer, S. Goldenstein ‘Automatic fruit and vegetable classification from images’, Computers and Electronics in Agriculture, 2010, 70, pp. 96– 104.
. A. Baltazar, J. I. Aranda, G. Gonzalez Aguilar, Bayesian classification of ripening stages of tomato fruit using acoustic impact and colorimeter sensor data, Computers and electronics in agriculture’, 2008, 60, pp. 113–121.
. S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M. P. Reyes, M.-L. Shyu, S.-C. Chen, S. Iyengar, ‘A survey on deep learning: Algorithms, techniques, and applications, ACM Computing Surveys (CSUR)’, 2018, 51, pp.51, 1–36
. Hussain, Israr, Qianhua He and Zhuliang Chen. ‘Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System. International Journal of Computer Science & Applications’, 2018, 8, pp: 01-14.
. I. Hussain, W. L. Wu, H. Q. Hua, N. Hussain, ‘Intra-class recognition of fruits using dcnn for commercial trace back-system, in: Proceedings of the 4th International Conference on Multimedia Systems and Signal Processing (MSSP)’, 2019, pp. 194–199.
. Mure¸San, Horea, and Mihai Oltean. ‘Fruit recognition from images using deep learning’, Acta Universitatis Sapientiae Informatica’, 2018, 10, pp. 26-42.
. Wan, Shaohua, and Sotirios Goudos. ‘Faster R-CNN for multi-class fruit detection using a robotic vision system’, Computer Networks, 2010, 168, pp. 107036.
. Ludwig, I. and Ludwig A. W. W ‘Kondo effect induced by a magnetic field’, Phys. Rev. B, 2001, 64, pp. 045328
. Israr Hussain, Qianhua He, Zhuliang Chen, & Wei Xie. (2018). Fruit Recognition dataset (Version V 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1310165
. Alvarez-Canchila, O. I., D. E. Arroyo-Pérez, A. Patino-Saucedo, H. Rostro González, and A. Patino-Vanegas. ‘Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning’, In Journal of Physics: Conference Series, 2020., vol. 1547, no. 1, p. 012020. IOP Publishing,
. Siddiqi, Raheel. ‘Effectiveness of Transfer Learning and Fine Tuning in Automated Fruit Image Classification’, In Proceedings of the 2019 3rd International Conference on Deep Learning Technologies, 2019 pp. 91-100.
. Tan, Chuanqi, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. ‘A survey on deep transfer learning’, In International conference on artificial neural networks, 2018, pp. 270-279.
Copyright (c) 2020 International Journal of Computer (IJC)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- By submitting the processing fee, it is understood that the author has agreed to our terms and conditions which may change from time to time without any notice.
- It should be clear for authors that the Editor In Chief is responsible for the final decision about the submitted papers; have the right to accept\reject any paper. The Editor In Chief will choose any option from the following to review the submitted papers:A. send the paper to two reviewers, if the results were negative by one reviewer and positive by the other one; then the editor may send the paper for third reviewer or he take immediately the final decision by accepting\rejecting the paper. The Editor In Chief will ask the selected reviewers to present the results within 7 working days, if they were unable to complete the review within the agreed period then the editor have the right to resend the papers for new reviewers using the same procedure. If the Editor In Chief was not able to find suitable reviewers for certain papers then he have the right to reject the paper.
- Author will take the responsibility what so ever if any copyright infringement or any other violation of any law is done by publishing the research work by the author
- Before publishing, author must check whether this journal is accepted by his employer, or any authority he intends to submit his research work. we will not be responsible in this matter.
- If at any time, due to any legal reason, if the journal stops accepting manuscripts or could not publish already accepted manuscripts, we will have the right to cancel all or any one of the manuscripts without any compensation or returning back any kind of processing cost.
- The cost covered in the publication fees is only for online publication of a single manuscript.