Fruit Freshness Detection Using Android-Based Transfer Learning MobileNetV2
Abstract
Abstract. Fruit is an important part of the source of food nutrition in humans. Fruit freshness is one of the most important factors in selecting fruit that is suitable for consumption. Fruit freshness is also an important factor in determining the price of fruit in the market. So it is very necessary to detect fruit freshness which can be done by machine. Take apples, bananas, and oranges as samples. The machine learning algorithm used in this study uses MobileNetV2 with transfer learning techniques. MobileNetV2 introduces many new ideas aimed at reducing the number of parameters to make it more efficient to run on mobile devices and achieve high classification accuracy. Transfer learning is used so that data does not need training from the start, so it only takes several networks from MobileNetV2 that have previously been trained and then retrained with a different purpose to improve accuracy results. Then the models that have been created are inserted into the application using Android Studio. Software testing is done through black box testing.
Purpose: The purpose of this research is to design a machine-learning model to detect fruit freshness and then apply it to application Android smartphones.
Methods/Study design/approach: The algorithm used in this study uses MobileNetV2 with transfer learning techniques. Models that have been created are inserted into the application using Android Studio.
Result/Findings: The training results using MobileNetV2 transfer learning obtained an accuracy of 99.62% and the loss results obtained were 0.34%. The results of the application after testing using the black box testing method required improvements to the application and the machine learning model so that it can run optimally.
Novelty/Originality/Value: Machine learning models that have been created using transfer learning MobileNetV2 are applied to Android applications so that they can be used by the public.
References
[2] A. Ren et al., “Machine Learning Driven Approach towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing,” IEEE Sens. J., vol. 20, no. 4, pp. 2075–2083, 2020, doi: 10.1109/JSEN.2019.2949528.
[3] F. N. Cahya, R. Pebrianto, and T. A. M, “Klasifikasi Buah Segar dan Busuk Menggunakan Ekstraksi Fitur Hu-Moment , Haralick dan Histogram,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 6, no. 1, pp. 57–62, 2021, doi: 10.31294/ijcit.v6i1.10052.
[4] N. Özkurt, T. Yıldırım, Yaşar Üniversitesi, Institute of Electrical and Electronics Engineers. Turkey Section., and Institute of Electrical and Electronics Engineers, 2019 Innovations in Intelligent Systems and Applications Conference (ASYU) : proceedings : 31 October-2 November 2019, Izmir, Turkey.
[5] K. Skedgell and C. A. Kearney, “Predictors of school absenteeism severity at multiple levels: A classification and regression tree analysis,” Child. Youth Serv. Rev., vol. 86, no. 2017, pp. 236–245, 2018, doi: 10.1016/j.childyouth.2018.01.043.
[6] N. Bindal, R. Modi, and B. K. Kaushik, “Fiscal classification using convolutional neural network,” in Emerging Topics in Artificial Intelligence 2020, Aug. 2020, no. July, p. 55. doi: 10.1117/12.2568314.
[7] W. Wang, Y. Li, T. Zou, X. Wang, J. You, and Y. Luo, “A novel image classification approach via dense-mobilenet models,” Mob. Inf. Syst., vol. 2020, 2020, doi: 10.1155/2020/7602384.
[8] A. Alsenan, B. Ben Youssef, and H. Alhichri, “MobileUNetV3—A Combined UNet and MobileNetV3 Architecture for Spinal Cord Gray Matter Segmentation,” Electron., vol. 11, no. 15, 2022, doi: 10.3390/electronics11152388.
[9] Q. Xiang, G. Zhang, X. Wang, J. Lai, R. Li, and Q. Hu, “Fruit image classification based on Mobilenetv2 with transfer learning technique,” ACM Int. Conf. Proceeding Ser., 2019, doi: 10.1145/3331453.3361658.
[10] S. Lenka, S. Kumar, S. Mishra, K. K. Jena, M. L. Liya, and ..., “4th International Conference on I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD),” IEEE Trans. Softw. Eng., pp. 73–79, 2019, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9243441/
[11] J. Li, L. Sun, Q. Yan, Z. Li, W. Srisa-An, and H. Ye, “Significant Permission Identification for Machine-Learning-Based Android Malware Detection,” IEEE Trans. Ind. Informatics, vol. 14, no. 7, pp. 3216–3225, 2018, doi: 10.1109/TII.2017.2789219.
[12] V. Ganesan, “Machine Learning in Mobile Applications,” Int. J. Comput. Sci. Mob. Comput., vol. 11, no. 2, pp. 110–118, 2022, doi: 10.47760/ijcsmc.2022.v11i02.013.
[13] V. Oliveira, L. Teixeira, and F. Ebert, “On the Adoption of Kotlin on Android Development: A Triangulation Study,” SANER 2020 - Proc. 2020 IEEE 27th Int. Conf. Softw. Anal. Evol. Reengineering, pp. 206–216, 2020, doi: 10.1109/SANER48275.2020.9054859.
[14] L. Ardito, R. Coppola, G. Malnati, and M. Torchiano, “Effectiveness of Kotlin vs. Java in android app development tasks,” Inf. Softw. Technol., vol. 127, p. 106374, 2020, doi: 10.1016/j.infsof.2020.106374.
[15] S. Krishnan, M. J. Franklin, K. Goldberg, J. Wang, and E. Wu, “ActiveClean: An interactive data cleaning framework for modern machine learning,” Proc. ACM SIGMOD Int. Conf. Manag. Data, vol. 26-June-20, pp. 2117–2120, 2016, doi: 10.1145/2882903.2899409.
[16] A. Shrivastava, R. Sohandani, and N. Khatwani, “Fresh and Stale Images of Fruits and Vegetables | Kaggle,” Kaggle, 2021. https://www.kaggle.com/datasets/raghavrpotdar/fresh-and-stale-images-of-fruits-and-vegetables (accessed Feb. 20, 2023).
[17] Sriram Reddy Kalluri, “Fruits fresh and rotten for classification,” 2018. https://www.kaggle.com/sriramr/fruits-fresh-and-rotten-for-classification
[18] V. R. Joseph and A. Vakayil, “SPlit: An Optimal Method for Data Splitting,” Technometrics, vol. 64, no. 2, pp. 166–176, 2022, doi: 10.1080/00401706.2021.1921037.
[19] E. Breck, N. Polyzotis, S. Roy, S. E. Whang, and M. Zinkevich, “Data Validation for Machine Learning,” Proc. Mach. Learn. Syst. 1 (MLSys 2019), pp. 334–347, 2019, [Online]. Available: https://proceedings.mlsys.org/paper/2019/file/5878a7ab84fb43402106c575658472fa-Paper.pdf
[20] S. Basso, A. Ceselli, and A. Tettamanzi, “Random sampling and machine learning to understand good decompositions,” Ann. Oper. Res., vol. 284, no. 2, pp. 501–526, 2020, doi: 10.1007/s10479-018-3067-9.
[21] A. Mikołajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” 2018 Int. Interdiscip. PhD Work. IIPhDW 2018, no. August 2019, pp. 117–122, 2018, doi: 10.1109/IIPHDW.2018.8388338.
[22] N. Hikmatia and M. Zul, “Aplikasi Penerjemah Bahasa Isyarat Indonesia menjadi Suara berbasis Android menggunakan Tensorflow,” J. Komput. Terap., vol. 7, no. Vol. 7 No. 1 (2021), pp. 74–83, 2021, doi: 10.35143/jkt.v7i1.4629.
[23] S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behav. Processes, vol. 148, pp. 56–62, 2018, doi: 10.1016/j.beproc.2018.01.004.
[24] S. Supriyono, “Software Testing with the approach of Blackbox Testing on the Academic Information System,” IJISTECH (International J. Inf. Syst. Technol., vol. 3, no. 2, pp. 227–233, 2020, [Online]. Available: https://ijistech.org/ijistech/index.php/ijistech/article/view/54
