Corn Disease Classification Using Transfer Learning and Convolutional Neural Network

Faisal Dharma Adhinata; Gita Fadila Fitriana; Aditya Wijayanto; Muhammad Pajar Kharisma Putra

doi:10.30595/juita.v9i2.11686

Authors

Faisal Dharma Adhinata Institut Teknologi Telkom Purwokerto http://orcid.org/0000-0002-2624-173X
Gita Fadila Fitriana Institut Teknologi Telkom Purwokerto
Aditya Wijayanto Institut Teknologi Telkom Purwokerto
Muhammad Pajar Kharisma Putra Universitas Teknokrat Indonesia

DOI:

https://doi.org/10.30595/juita.v9i2.11686

Keywords:

Convolutional Neural Network, corn plant, DenseNet201, flatten layer, global average pooling layer

Abstract

Indonesia is an agricultural country with abundant agricultural products. One of the crops used as a staple food for Indonesians is corn. This corn plant must be protected from diseases so that the quality of corn harvest can be optimal. Early detection of disease in corn plants is needed so that farmers can provide treatment quickly and precisely. Previous research used machine learning techniques to solve this problem. The results of the previous research were not optimal because the amount of data used was slightly and less varied. Therefore, we propose a technique that can process lots and varied data, hoping that the resulting system is more accurate than the previous research. This research uses transfer learning techniques as feature extraction combined with Convolutional Neural Network as a classification. We analysed the combination of DenseNet201 with a Flatten or Global Average Pooling layer. The experimental results show that the accuracy produced by the combination of DenseNet201 with the Global Average Pooling layer is better than DenseNet201 with Flatten layer. The accuracy obtained is 93% which proves the proposed system is more accurate than previous studies.

Author Biographies

Faisal Dharma Adhinata, Institut Teknologi Telkom Purwokerto

Faculty of Informatics, Institut Teknologi Telkom Purwokerto, Indonesia

Gita Fadila Fitriana, Institut Teknologi Telkom Purwokerto

Faculty of Informatics, Institut Teknologi Telkom Purwokerto, Indonesia

Aditya Wijayanto, Institut Teknologi Telkom Purwokerto

Faculty of Informatics, Institut Teknologi Telkom Purwokerto, Indonesia

Muhammad Pajar Kharisma Putra, Universitas Teknokrat Indonesia

Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia

References

[1] D. Pitaloka, “Hortikultura: Potensi, Pengembangan Dan Tantangan,” Jurnal Teknologi Terapan: G-Tech, vol. 1, no. 1, pp. 1–4, 2020.

[2] R. P. Ramadhan and N. L. Marpaung, “Identifikasi jenis penyakit daun tanaman jagung menggunakan jaringan saraf tiruan berbasis backpropagation,” Jom FTEKNIK, vol. 6, no. 1, pp. 1–5, 2019.

[3] W. Setiawan, M. Syarief, and N. Prastiti, “Maize Leaf Disease Image Classification Using Bag of Features,” Jurnal Infotel, vol. 11, no. 2, pp. 48–54, 2019.

[4] M. Syarief and W. Setiawan, “Convolutional neural network for maize leaf disease image classification,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 3, pp. 1376–1381, 2020.

[5] A. Hidayat, U. Darusalam, and I. Irmawati, “Detection of Disease on Corn Plants Using Convolutional Neural Network Methods,” Jurnal Ilmu Komputer dan Informasi, vol. 12, no. 1, pp. 51, 2019.

[6] M. Dyrmann, H. Karstoft, and H. S. Midtiby, “Plant species classification using deep convolutional neural network,” Biosystems Engineering, vol. 151, pp. 72–80, 2016.

[7] O. Sudana, I. W. Gunaya, and I. K. G. D. Putra, “Handwriting identification using deep convolutional neural network method,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 4, pp. 1934–1941, 2020.

[8] R. Prathivi, “Optimasi Model TL-CNN Untuk Klasifikasi Citra CIFAR-10,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 1, no. 10, pp. 3–7, 2021.

[9] Stephen, Raymond, and H. Santoso, “Aplikasi Convolutional Neural Network untuk Mendeteksi Jenis-jenis Sampah,” Explore: Jurnal Sistem informasi dan telematika (Telekomunikasi, Multimedia dan Informatika), vol. 10, no. 2, pp. 122–130, 2019.

[10] F. D. Adhinata, D. P. Rakhmadani, M. Wibowo, and A. Jayadi, “A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face,” JUITA: Jurnal Informatika, vol. 9, no. 1, pp. 115–121, 2021.

[11] D. Han, Q. Liu, and W. Fan, “A new image classification method using CNN transfer learning and web data augmentation,” Expert Systems with Applications, vol. 95, pp. 43–56, 2018.

[12] S. Giri and B. Joshi, “Transfer Learning Based Image Visualization Using CNN,” International Journal of Artificial Intelligence & Applications, vol. 10, no. 4, pp. 47–55, 2019.

[13] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 2261–2269, 2017.

[14] D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, “PlantDoc: A dataset for visual plant disease detection,” ACM International Conference Proceeding Series, February, pp. 249–253, 2020.

[15] G. G. and A. P. J., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Computers & Electrical Engineering, vol. 76, pp. 323–338, 2019.

[16] A. Patil and M. Rane, “Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition,” Smart Innovation, Systems and Technologies, vol. 195, pp. 21–30, 2021.

[17] Y.-D. Zhang, C. Pan, X. Chen, and F. Wang, “Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling,” Journal of Computational Science, vol. 27, pp. 57–68, 2018.

[18] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.