Grape disease detection using dual channel Convolution Neural Network method

Authors

  • Mawaddah Harahap Universitas Prima Indonesia
  • Valencia Angelina Universitas Prima Indonesia
  • Fenny Juliani Universitas Prima Indonesia
  • Celvin Universitas Prima Indonesia
  • Oscar Evander Universitas Prima Indonesia

DOI:

10.33395/sinkron.v5i2.10939

Keywords:

Digital Image, Dual-Channel Convolution Neural Network, Gabor Filter Method, Grape Disease, Segmentation Based Fractal Co-Occurrence Texture Analysis Method

Abstract

Grapes are one type of fruit that is usually used to make grape juice, jelly, grapes, grape seed oil and raisins, or to be eaten directly. So far, checking for disease in grapes is still done manually, by checking the leaves of the grapes by experts. This method certainly takes a long time considering the extent of the vineyards that must be evaluated. To solve this problem, it is necessary to apply a method of detecting grape disease, so that it can help the common people to detect grape disease. This research will use the Dual-Channel Convolutional Neural Network method. The process of detecting grape disease using the DCCNN method will begin with the extraction of the leaves from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to extract the features, color, and texture of the extracted leaves. The result is the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method. However, more datasets will cause the execution process to take longer. Changes in the angle and frequency values in the Gabor method at the time of testing will reduce the accuracy of the test results. The conclusion of this study are the DCCNN method can be used to detect the type of leaf disease in grapes and the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Abhirawan, H., Jondri, & Arifianto, A. (2017). Pengenalan Wajah Menggunakan Convolutional Neural Networks (CNN). e-Proceeding of Engineering, 4(3), 4907–4916.

Chen, Y., Wang, J., Chen, X., Sangaiah, A. K., Yang, K., & Cao, Z. (2019). Image super-resolution algorithm based on dual-channel convolutional neural networks. Applied Sciences (Switzerland), 9(11). https://doi.org/10.3390/app9112316

Damayanti, F., Muntasa, A., Herawati, S., Yusuf, M., & Rachmad, A. (2020). Identification of Madura Tobacco Leaf Disease Using Gray-Level Co-Occurrence Matrix, Color Moments and Naïve Bayes. Journal of Physics: Conference Series, 1477(5). https://doi.org/10.1088/1742-6596/1477/5/052054

Felix, Faisal, S., Butarbutar, T. F. M., & Sirait, P. (2019). Implementasi CNN dan SVM untuk Identifikasi Penyakit Tomat via Daun. 20(2), 117–134.

Ghoury, S., Sungur, C., & Durdu, A. (2019). Real-Time Diseases Detection of Grape and Grape Leaves using Faster R-CNN and SSD MobileNet Architectures. (Icatces), 39–44.

Hidayati P.I. (2018). Analisis Hama pada Tanaman Anggur dengan Pendekatan Metode CF (Certainty Factor) Berbasis Mobile Android. SMATIKA Jurnal, 8(2).

Ji, M., Zhang, L., & Wu, Q. (2020). Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Information Processing in Agriculture, 7(3), 418–426. https://doi.org/10.1016/j.inpa.2019.10.003

Leonardo, L. (2020). Penerapan Metode Filter Gabor Untuk Analisis Fitur Tekstur Citra Pada Kain Songket. Jurnal Sistem Komputer dan Informatika (JSON), 1(2), 120. https://doi.org/10.30865/json.v1i2.1942

Mardiyah, Basri, Z., Yusuf, R., & Hawalina. (2017). Pertumbuhan Tunas Anggur Hitam (Vitis vinifera L.) pada Berbagai Konsentrasi Benzylamino Purin dan Indolebutyric Acid. Jurnal Agroland, 24(3), 181–189.

Marhumah, S., Rahayu, T., & Hayati, A. (2016). PERASAN MACAM BUAH ANGGUR (Vitis vinivera L.) SEBAGAI PENETRALISIR MERKURI (Hg) DENGAN METODE UVAL. Biosaintropis (Bioscience-Tropic), 2(1), 25–36.

Narvekar, P., Kumbhar, M. M., & Patil, S. N. (2014). Grape Leaf Diseases Detection & Analysis using CCM Method. International Journal of Computer Engineering and Applications, VI(Ii), 3365–3372.

Saharia, A., & Kedia, Y. (2016). A Review paper on Artificial Neural Networks. 3(8), 65–71.

SJ, P. W. P., Priantoro, A. T., & Setiyati, C. R. H. (2013). Terhadap Per Tumbuhan Tanaman Anggur ( Vitis Vinifera ). Penelitian, 19(1), 87–101.

Sugiartha, I. G. R. A., Sudarma, M., & Widyantara, I. M. O. (2016). Ekstraksi Fitur Warna, Tekstur dan Bentuk untuk Clustered-Based Retrieval of Images (CLUE). Majalah Ilmiah Teknologi Elektro, 16(1), 85. https://doi.org/10.24843/mite.1601.12

Sugiarti, L. (2017). Analisis Tingkat Keparahan Penyakit Karat Winaya Mukti Tanjungsari. Jagros, 1(2), 80–89.

Downloads


Crossmark Updates

How to Cite

Harahap, M., Angelina, V., Juliani, F. ., Celvin, & Evander, O. (2021). Grape disease detection using dual channel Convolution Neural Network method . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(2), 314-324. https://doi.org/10.33395/sinkron.v5i2.10939