Development of Apple Fruit Classification System using Convolutional Neural Network (CNN) MobileNet Architecture on Android Platform

Masparudin Masparudin, Iskandar Fitri, Sumijan Sumijan

Abstract


In the current digital era, image classification of fruits, particularly apples, has become crucial for various applications, ranging from agriculture to retail. This research focuses on the utilization of Convolutional Neural Network (CNN) with the MobileNet architecture to classify apple fruit images. Using the Python programming language, three models were successfully trained: Model 1 for apple fruit types, Model 2 for apple fruit diseases, and Model 3 for apple fruit ripeness levels. All three models underwent training and validation, with the final results at epoch 10: Model 1 for apple types achieved an accuracy of 100% and a loss of 0.0046, Model 2 for apple diseases achieved an accuracy of 100% and a loss of 0.0075, while Model 3 for apple ripeness levels achieved an accuracy of 99.76% and a loss of 0.0439. Subsequently, these models were tested on an Android device, and there were two testing scenarios. In the first scenario, each model was tested with 15 images individually. The results showed 100% accuracy for Models 1 and 2, while Model 3 achieved a lower accuracy of 86.67%. In the second scenario, all three models were tested simultaneously using 30 test images, resulting in an accuracy of 55.55%. Several factors, such as limitations in the apple image dataset, particularly in the ripeness dataset, object backgrounds, image capture distances, color and texture similarities, as well as lighting quality, influenced the classification outcomes. To enhance future performance, improved data preprocessing and a combination of detection and classification techniques are needed. This research provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.provides valuable insights for researchers and practitioners looking to implement image classification technology in real-world applications.

Full Text:

PDF

References


M. Tripathi, "Analysis of convolutional neural network based image classification techniques," Journal of Innovative Image Processing (JIIP), vol. 3, no. 02, pp. 100-117, 2021.

Naranjo-Torres, José, et al. "A review of convolutional neural network applied to fruit image processing." Applied Sciences 10.10 (2020): 3443.

Ratnawati, Luthfiana, and Dwi Ratna Sulistyaningrum. "Penerapan random forest untuk mengukur tingkat keparahan penyakit pada daun apel." Jurnal Sains dan Seni ITS 8.2 (2020): A71-A77.

Razka, Muhammad Hanif, et al. "Klasifikasi Tingkat Kematangan Buah Apel Berdasarkan Fitur Warna Menggunakan Algoritma K-Nearest Neighbor dan Ekstraksi Warna HSV." Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya. Vol. 2. No. 2. 2021.

W. Han et al., "Contextnet: Improving convolutional neural networks for automatic speech recognition with global context," arXiv preprint arXiv:2005.03191, 2020.

L. Chen et al., "Review of image classification algorithms based on convolutional neural networks," Remote Sensing, vol. 13, no. 22, pp. 4712, 2021.

W. Wang et al., "A new image classification approach via improved MobileNet models with local receptive field expansion in shallow layers," Computational Intelligence and Neuroscience, 2020.

Y. D. Zhang, S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S. H. Wang, "Improved breast cancer classification through combining graph convolutional network and convolutional neural network," Information Processing & Management, vol. 58, no. 2, pp. 102439, 2021.

V. R. Allugunti, "A machine learning model for skin disease classification using convolution neural network," International Journal of Computing, Programming and Database Management, vol. 3, no. 1, pp. 141-147, 2022.

Y. Kaya and E. Gürsoy, "A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection," Soft Computing, vol. 27, no. 9, pp. 5521-5535, 2023.

Pratama, Fandy Indra, et al. "Klasifikasi Kematangan Buah Apel Berdasarkan Warna Dan Tekstur Menggunakan Algoritma K-Nearest Neighbor." Jurnal Ilmiah Intech: Information Technology Journal of UMUS 5.1 (2023): 11-18.

P. N. Srinivasu et al., "Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM," Sensors, vol. 21, no. 8, pp. 2852, 2021.

Chen, Junde, et al. "Identification of plant disease images via a squeeze‐and‐excitation MobileNet model and twice transfer learning." IET Image Processing 15.5 (2021): 1115-1127.

Tripathi, Milan. "Analysis of convolutional neural network based image classification techniques." Journal of Innovative Image Processing (JIIP) 3.02 (2021): 100-117.

Michele, Aurelia, Vincent Colin, and Diaz D. Santika. "Mobilenet convolutional neural networks and support vector machines for palmprint recognition." Procedia Computer Science 157 (2019): 110-117.

Swasono, Dwiretno Istiyadi, Mohammad Abuemas Rizq Wijaya, and Muhamad Arief Hidayat. "Klasifikasi Penyakit pada Citra Buah Jeruk Menggunakan Convolutional Neural Networks (CNN) dengan Arsitektur Alexnet." INFORMAL: Informatics Journal 8.1 (2023): 68-75.

Pah, Nikotesa Eko Rianto, Sebastianus AS Mola, and Arfan Y. Mauko. "Ekstrasi Ciri Warna Hsv Dan Ciri Bentuk Moment Invariant Untuk Klasifikasi Buah Apel Merah." J-Icon: Jurnal Komputer dan Informatika 9.2 (2021): 142-153.

Paliwang, Andi Asrafil Ardan, et al. "Klasifikasi Penyakit Tanaman Apel Dari Citra Daun Dengan Convolutional Neural Network." Sebatik 24.2 (2020): 207-212.

Siswanto, Irwan, Ema Utami, and Suwanto Raharjo. "Klasifikasi tingkat kematangan buah berdasarkan warna dan tekstur menggunakan metode k-nearest neighbor dan nearest mena classifier." Inspiration: Jurnal Teknologi Informasi dan Komunikasi 10.1 (2020): 93-101.




DOI: https://doi.org/10.32520/stmsi.v13i1.3533

Article Metrics

Abstract view : 214 times
PDF - 80 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.