Comparison Study of Convolutional Neural Network Architecture in Aglaonema Classification

Yessi Mulyani, Dzihan Septiangraini, Meizano Ardhi Muhammad, Gigih Forda Nama

Abstract


Convolutional Neural Network (CNN) is very good at classifying images. To measure the best CNN architecture, a study must be done against real-case scenarios. Aglaonema, one of the plants with high similarity, is chosen as a test case to compare CNN architecture. In this study, a classification process was carried out on five classes of Aglaonema imagery by comparing five architectures from the Convolutional Neural Network (CNN) method: LeNet, AlexNet, VGG16, Inception V3, and ResNet50. The total dataset used is 500 image data, with the distribution of training data by 80% and test data by 20%. The segmentation process is performed using the Grabcut algorithm by separating the foreground and background. To build a model for CNN architecture using Google Colab and Google Drive storage. The results of the tests carried out on five classes of Aglaonema images obtained the best accuracy, precision, and recall results on the Inception V3 architecture with values of 92.8%, 93%, and 92.8%. The CNN architecture has the highest level of accuracy in classifying aglaonema plant types based on images. This study seeks to close research gaps, contribute to the field of research, and serve as a platform for primary prevention research.

Keywords


CNN; Aglaonema; LeNet; AlexNet; VGG16; Inception V3; ResNet50

Full Text:

PDF

References


L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.

A. Anton, N. F. Nissa, A. Janiati, N. Cahya, and P. Astuti, “Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification,” Sci. J. Informatics, vol. 8, no. 1, pp. 144–153, 2021, doi: 10.15294/sji.v8i1.26888.

M. I. Mardiyah and T. Purwaningsih, “Developing deep learning architecture for image classification using convolutional neural network (CNN) algorithm in forest and field images,” Sci. Inf. Technol. Lett., vol. 1, no. 2, pp. 83–91, 2020, doi: 10.31763/sitech.v1i2.160.

R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition,” Smart Innov. Syst. Technol., vol. 195, no. 9, pp. 61–629, 2021, doi: 10.1007/978-981-15-7078-0_3.

A. Biswas and M. S. Islam, “An Efficient CNN Model for Automated Digital Handwritten Digit Classification,” J. Inf. Syst. Eng. Bus. Intell., vol. 7, no. 1, p. 42, 2021, doi: 10.20473/jisebi.7.1.42-55.

O. I. Abiodun et al., “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition,” IEEE Access, vol. 7, no. February 2017, pp. 158820–158846, 2019, doi: 10.1109/ACCESS.2019.2945545.

D. Seo, J. Ahn, and H. Nam, “Comparison of CNN Architectures using RP Algorithm for Burst Signal Detection,” Int. Conf. ICT Converg., vol. 2020-Octob, pp. 809–812, 2020, doi: 10.1109/ICTC49870.2020.9289320.

“No Title,” 2019.

“Buku Budidaya Tanaman Hias Daun Athurium dan Aglonema.pdf.”

C. Of et al., “Karakterisasi tanaman aglaonema di dataran tinggi rejang lebong (,” vol. 17, no. 2, pp. 141–151, 2019.

W. S. Eka Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.

I. A. Sabilla, “Arsitektur Convolutional Neural Network (Cnn) Untuk Klasifikasi Jenis Dan Kesegaran Buah Pada Neraca Buah,” Tesis, no. 201510370311144, pp. 1–119, 2020.

S. Muhammad and A. T. Wibowo, “Klasifikasi Tanaman Aglaonema Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network (Cnn,” e-Proceeding Eng., vol. 8, no. 5, pp. 10621–10636, 2021.

H.Taufiq, K. Patmi, and S. Ardi, “Classification of Aglaonema Plants Berdasarkan Corak Daun,” Semin. Nas. Inov. Teknol., pp. 223–228, 2019, [Online]. Available: https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/541

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, A survey of the recent architectures of deep convolutional neural networks, vol. 53, no. 8. Springer Netherlands, 2020. doi: 10.1007/s10462-020-09825-6.

N. Anantrasirichai, S. Hannuna, and N. Canagarajah, “Automatic Leaf Extraction from Outdoor Images,” no. September, 2017, [Online]. Available: http://arxiv.org/abs/1709.06437

H. Wu, Y. Liu, X. Xu, and Y. Gao, “Object Detection Based on the GrabCut Method for Automatic Mask Generation,” Micromachines, vol. 13, no. 12, 2022, doi: 10.3390/mi13122095.

L. Zhang and M. Chang, “An image inpainting method for object removal based on difference degree constraint,” Multimed. Tools Appl., vol. 80, no. 3, pp. 4607–4626, 2021, doi: 10.1007/s11042-020-09835-0.

S. Kulkarni, “Removal of Unwanted Objects From Images Using Statistics,” ICTACT J. Image Video Process., vol. 9, no. 2, pp. 1887–1893, 2018, doi: 10.21917/ijivp.2018.0268.

V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, “Efficient Processing of Deep Neural Networks,” Synth. Lect. Comput. Archit., vol. 15, no. 2, pp. 1–341, 2020, doi: 10.2200/s01004ed1v01y202004cac050.

D. Irfansyah, M. Mustikasari, and A. Suroso, “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” J. Inform. J. Pengemb. IT, vol. 6, no. 2, pp. 87–92, 2021.

S. Mariyam, A. Osman, and C. Ramadhe, “Weight Changes for Learning Mechanisms in Two-Term Back-Propagation Network,” Artif. Neural Networks - Archit. Appl., 2013, doi: 10.5772/51776.

A. H. Sianturi, “Implementasi Algoritma Convolutional Neural Networks di Microsoft Azure untuk Mendeteksi Jenis Kebutaan Mata yang dialami Penderita Penyakit Diabetes,” Anal. Kesadahan Total dan Alkalinitas pada Air Bersih Sumur Bor dengan Metod. Titrim. di PT Sucofindo Drh. Provinsi Sumatera Utara, pp. 44–48, 2018.

A. Salma, A. Bustamam, and D. Sarwinda, “Diabetic Retinopathy Detection Using GoogleNet Architecture of Convolutional Neural Network Through Fundus Images,” vol. 2021, pp. 1–6, 2021, doi: 10.11594/nstp.2021.0701.

Z. Niswati, R. Hardatin, M. N. Muslimah, and S. N. Hasanah, “Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear,” Fakt. Exacta, vol. 14, no. 3, p. 160, 2021, doi: 10.30998/faktorexacta.v14i3.10010.




DOI: http://dx.doi.org/10.24042/ijecs.v2i2.13694

Refbacks

  • There are currently no refbacks.


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

Creative Commons License

International Journal of Electronics and Communications System (IJECS) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.