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Classification of Coffee Leaf Diseases using CNN
Corresponding Author(s) : Yufis Azhar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 3, August 2023
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.
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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.
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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
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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
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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