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  3. Vol. 8, No. 3, August 2023
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Vol. 8, No. 3, August 2023

Issue Published : Aug 31, 2023
Creative Commons License

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

Classification of Coffee Leaf Diseases using CNN

https://doi.org/10.22219/kinetik.v8i3.1745
Dara Sucia
Universitas Muhammadiyah Malang
Auliya Tara Shintya Larasabi
Universitas Muhammadiyah Malang
Yufis Azhar
Universitas Muhammadiyah Malang
Zamah Sari
Universitas Muhammadiyah Malang

Corresponding Author(s) : Yufis Azhar

yufis@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 8, No. 3, August 2023
Article Published : Aug 31, 2023

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Abstract

Indonesia’s coffee industry plays a crucial role as a major export, making a significant contribution to the country’s economy by generating foreign exchange. The quality and quantity of coffee production depend on various factors such as humidity, rain, and fungus that can cause rust diseases on coffee leaves. These diseases can spread quickly and affect other coffee plants quality, leading to decreased production. To address this issue, CNN with VGG-19 architecture model was utilized to identify coffee plant diseases using image data and the python programming language, which in previous studies used MATLAB as their platform. In addition, VGG-19 with image enhancement and contouring data for pre-processing step has a more profound learning feature than the method used in the previous studies, AlexNet which makes the structure of VGG- 19 more detailed. The dataset used in this paper is Robusta Coffee Leaf Images Dataset which have three classes, namely health, red spider mite, and rust. The VGG-19 model attained F1-Score of 90% when evaluated using the testing data with ratio 80:20, where 80% is training data, and 20% is validation data. This paper employed 0.0001 learning rate, batch size 15, momentum 0.9, 12 training iteration, and RMSprop optimizer.

Keywords

Images Classification Coffee Leave Diseases Deep Learning CNN VGG-19
Sucia, D., Shintya Larasabi , A. T. ., Azhar, Y., & Sari, Z. (2023). Classification of Coffee Leaf Diseases using CNN. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 8(3). https://doi.org/10.22219/kinetik.v8i3.1745
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Read More

References


BPS, ”Statistik Kopi Indonesia,” Badan Pusat Statistik Indonesia, 2021.

D. Irfansyah et al., “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” vol. 6, no. 2, pp. 87–92, 2021.

M. Ilhamsyah and U. Enri, “Identification Of Bacterial Spot Diseases On Paprika Leaves Using CNN And Transfer Learning,” vol. 18, no. 1, 2022. https://doi.org/10.33480/pilar.v18i1.2755

A. Waheed, M. Goyal, D. Gupta, A. Khanna, and A. Ella, “An optimized dense convolutional neural network model for disease recognition and classification in corn leaf,” Comput. Electron. Agric., vol. 175, no. April, p. 105456, 2020. https://doi.org/10.1016/j.compag.2020.105456

M. A. Hasan, Y. Riyanto, and D. Riana, “Klasifikasi penyakit citra daun anggur menggunakan model CNN-VGG16 Grape leaf image disease classification using CNN-VGG16 model,” vol. 9, no. December 2020, pp. 218–223, 2021. https://doi.org/10.14710/jtsiskom.2021.14013

A. S. Paymode and V. B. Malode, “Arti fi cial Intelligence in Agriculture Transfer Learning for Multi-Crop Leaf Disease Image Classi fi cation using Convolutional Neural Network VGG,” Artif. Intell. Agric., vol. 6, pp. 23–33, 2022. https://doi.org/10.1016/j.aiia.2021.12.002

G. M. Esgario, P. B. C. De Castro, L. M. Tassis, and R. A. Krohling, “An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning,” no. xxxx, pp. 1–10, 2021. https://doi.org/10.1016/j.inpa.2021.01.004

N. U. R. Ibrahim, G. A. Y. U. Lestary, and F. S. Hanafi, “Klasifikasi Tingkat Kematangan Pucuk Daun Teh menggunakan Metode Convolutional Neural Network,” vol. 10, no. 1, pp. 162–176, 2022.

F. Teknologi, I. Universitas, and J. Barat, “Klasifikasi Jenis Citra Daun Mangga Menggunakan Convolutional Neural Network,” pp. 223–238, 2020.

R. Sistem and U. M. Malang, “Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model,” vol. 5, no. 158, pp. 9–11, 2021.

A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola, “Dive into Deep Learning,” 2022.

A. Luque-chang, E. Cuevas, M. Pérez-cisneros, F. Fausto, A. Valdivia-gonzález, and R. Sarkar, “Knowledge-Based Systems Moth Swarm Algorithm for Image Contrast Enhancement,” Knowledge-Based Syst., vol. 212, p. 106607, 2021. https://doi.org/10.1016/j.knosys.2020.106607

J. V. C. I. R, “End-to-end single image enhancement based on a dual network cascade model q,” J. Vis. Commun. Image Represent., vol. 61, pp. 284–295, 2019. https://doi.org/10.1016/j.jvcir.2019.04.008

V. Tyagi, “Understanding Digital Image Processing,” no. November, 2018. https://doi.org/10.1201/9781315123905

D. M. Hakim et al., “Convolutional Neural Network untuk Pengenalan Citra Notasi Musik,” vol. 18, no. 3, pp. 214–226, 2019.

I. Type, R. Bruno, E. Jorge, S. Reader, and C. Science, “Detection of Brain Tumor in Magnetic Resonance Imaging ( MRI ) Images using Fuzzy C-Means and Thresholding Detection of Brain Tumor in Magnetic Resonance Imaging ( MRI ) Department of Computer Sciences Utica , New York In Partial fulfillment Of the Requirements of the Master of Science Degree,” 2023.

N. Modrzyk, OpenCV on the JavaVM.

E. Mehdi, M. Lachgar, H. Hrimech, and A. Kartit, “Arti fi cial Intelligence in Agriculture Optimization techniques in deep convolutional neuronal networks applied to olive diseases classi fi cation,” Artif. Intell. Agric., vol. 6, pp. 77–89, 2022. https://doi.org/10.1016/j.aiia.2022.06.001

E. Bisong, Building Machine Learning and Deep Learning Models on Google Cloud Platform.

S. Cheng and G. Zhou, “Facial Expression Recognition Method Based on Improved VGG Convolutional Neural Network,” vol. 34, no. 7, pp. 1– 16, 2020. https://doi.org/10.1142/S0218001420560030

N. Krishnamoorthy, L. V. N. Prasad, C. S. P. Kumar, B. Subedi, H. Baraki, and V. E. Sathishkumar, “Rice leaf diseases prediction using deep neural networks with transfer learning,” Environ. Res., vol. 198, no. May, p. 111275, 2021. https://doi.org/10.1016/j.envres.2021.111275

Keras, “About Keras.”

R. Gabriela and C. Dobre, “ResNet interpretation methods applied to the classification of foliar diseases in sunflower,” J. Agric. Food Res., vol. 9, no. 313, p. 100323, 2022. https://doi.org/10.1016/j.jafr.2022.100323

Z. Yan, H. Liu, T. Li, J. Li, and Y. Wang, “Two dimensional correlation spectroscopy combined with ResNet : Efficient method to identify bolete species compared to traditional machine learning,” LWT, vol. 162, no. December 2021, p. 113490, 2022. https://doi.org/10.1016/j.lwt.2022.113490

N. Razfar, J. True, R. Bassiouny, V. Venkatesh, and R. Kashef, “Weed detection in soybean crops using custom lightweight deep learning models,” J. Agric. Food Res., vol. 8, no. May 2021, p. 100308, 2022. https://doi.org/10.1016/j.jafr.2022.100308

A. Beikmohammadi, K. Faez, and A. Motallebi, “SWP-LeafNET : A novel multistage approach for plant leaf identification based on deep CNN,”

Expert Syst. Appl., vol. 202, no. May, p. 117470, 2022. https://doi.org/10.1016/j.eswa.2022.117470

A. Sagar and D. Jacob, “On Using Transfer Learning for Plant Disease Detection,” no. July, 2020. http://dx.doi.org/10.13140/RG.2.2.12224.15360/1

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KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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