Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network

Tsabitah Ayu, Vizza Dwi, Agus Eko Minarno

Abstract


Pertanian adalah salah satu sektor ekonomi yang terpenting di negara-negara Asia Tenggara. Saat ini, pembangunan ekonomi sangat bergantung pada pertanian. Seperti contoh Mangga, Manga juga merupakan bahan makanan yang dapat diolah menjadi berbagai jenis makanan yang lezat. Karena banyaknya manfaat pada buah ini tak jarang masyarakat ingin menanam pohon mangga untuk dibudidayakan dengan tujuan komersil maupun pribadi. Salah satu masalah utama yang menurunkan kualitas dan kuantitas manufaktur pertanian adalah penyakit tanaman. Oleh karena itu bidang penelitian pertanian menarik para peneliti dan ilmuwan untuk memberikan teknik untuk mengidentifikasi penyakit tanaman dengan menggunakan pengolahan gambar dan visi komputer seperti dalam kertas ini yang menggunaka model Convolutional Neural Network (CNN) untuk mengklasifikasi jenis daun mangga yang sakit (terserang hama) dan sehat berdasarkan bentuk dan tekstur daun. Pada penelitian yang dihasilkan tingkat akurasi sebesar 0,96.

Keywords


CNN, Penyakit Daun Mangga, Klasifikasi.

Full Text:

PDF

References


K. Trang, L. Tonthat, N. Gia Minh Thao, and N. Tran Ta Thi, “Mango Diseases Identification by a Deep Residual Network with Contrast Enhancement and Transfer Learning,” 2019 IEEE Conf. Sustain. Util. Dev. Eng. Technol. CSUDET 2019, pp. 138–142, 2019, doi: 10.1109/CSUDET47057.2019.9214620.

Mekarsari, “Mangga,” mekarsari.com. https://mekarsari.com/web/agro/mangga/ (accessed Dec. 04, 2020).

Z. Iqbal, M. A. Khan, M. Sharif, J. H. Shah, M. H. ur Rehman, and K. Javed, “An automated detection and classification of citrus plant diseases using image processing techniques: A review,” Comput. Electron. Agric., vol. 153, no. September 2017, pp. 12–32, 2018, doi: 10.1016/j.compag.2018.07.032.

Y. Zhang, Q. Hua, D. Xu, H. Li, Y. Bu, and P. Zhao, “A Complex-Valued CNN for Different Activation Functions in Polarsar Image Classification,” Int. Geosci. Remote Sens. Symp., pp. 10023–10026, 2019, doi: 10.1109/IGARSS.2019.8898534.

S. Islam and N. Jahan, “A Computer Vision Approach to Classify Local Flower using Convolutional Neural Network,” no. May, 2020, doi: 10.1109/ICICCS48265.2020.9121143.

A. Y. Wicaksono, N. Suciati, C. Fatichah, K. Uchimura, and G. Koutaki, “Modified Convolutional Neural Network Architecture for Batik Motif Image Classification,” IPTEK J. Sci., vol. 2, no. 2, pp. 26–30, 2017, doi: 10.12962/j23378530.v2i2.a2846.

A. S. M. F. Al Haque, M. R. Rahman, A. Al Marouf, and M. A. A. Khan, “A Computer Vision System for Bangladeshi Local Mango Breed Detection using Convolutional Neural Network (CNN) Models,” 2019 4th Int. Conf. Electr. Inf. Commun. Technol. EICT 2019, no. December, pp. 1–6, 2019, doi: 10.1109/EICT48899.2019.9068830.

S. Lu, Z. Lu, S. Aok, and L. Graham, “Fruit Classification Based on Six Layer Convolutional Neural Network,” Int. Conf. Digit. Signal Process. DSP, vol. 2018-Novem, pp. 1–5, 2019, doi: 10.1109/ICDSP.2018.8631562.

M. Sardogan, A. Tuncer, and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” UBMK 2018 - 3rd Int. Conf. Comput. Sci. Eng., pp. 382–385, 2018, doi: 10.1109/UBMK.2018.8566635.

C. K. Dewa, A. L. Fadhilah, and A. Afiahayati, “Convolutional Neural Networks for Handwritten Javanese Character Recognition,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 12, no. 1, p. 83, 2018, doi: 10.22146/ijccs.31144.

D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 3642–3649, 2012, doi: 10.1109/CVPR.2012.6248110.

C. K. Dewa and Afiahayati, “Suitable CNN Weight Initialization and Activation Function for Javanese Vowels Classification,” Procedia Comput. Sci., vol. 144, pp. 124–132, 2018, doi: 10.1016/j.procs.2018.10.512.

Missinglink.ai, “7 Types of Neural Network Activation Functions: How to Choose?,” Missinglink.ai. https://missinglink.ai/guides/neural-network-concepts/7-types-neural-network-activation-functions-right/.

A. S. Sri Widaningsih, “Klasifikasi Jurnal Ilmu Komputer Berdasarkan Pembagian,” Semin. Nas. Teknol. Inf. dan Komun. 2018 (SENTIKA 2018), vol. 2018, no. Sentika, pp. 320–328, 2018.

S. Ahmed Medjahed, “A Comparative Study of Feature Extraction Methods in Images Classification,” Int. J. Image, Graph. Signal Process., vol. 7, no. 3, pp. 16–23, 2015, doi: 10.5815/ijigsp.2015.03.03.




DOI: https://doi.org/10.24114/cess.v6i2.22857

Article Metrics

Abstract view : 511 times
PDF - 722 times

Refbacks



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

CESS (Journal of Computer Engineering, System and Science)

Creative Commons License
CESS (Journal of Computer Engineering, System and Science) is licensed under a Creative Commons Attribution 4.0 International License