Plant Diseases Classification based Leaves Image using Convolutional Neural Network
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M. S. P. Ngongoma, M. Kabeya, and K. Moloi, “A Review of Plant Disease Detection Systems for Farming Applications,” Appl. Sci., vol. 13, no. 10, p. 5982, May 2023, doi: 10.3390/app13105982.
M. Jung et al., “Construction of deep learning-based disease detection model in plants,” Sci. Rep., vol. 13, no. 1, p. 7331, May 2023, doi: 10.1038/s41598-023-34549-2.
I. Ahmed and P. K. Yadav, “A systematic analysis of machine learning and deep learning based approaches for identifying and di-agnosing plant diseases,” Sustain. Oper. Comput., vol. 4, no. February, pp. 96–104, 2023, doi: 10.1016/j.susoc.2023.03.001.
C. Jackulin and S. Murugavalli, “A comprehensive review on detection of plant disease using machine learning and deep learning approaches,” Meas. Sensors, vol. 24, no. July, p. 100441, 2022, doi: 10.1016/j.measen.2022.100441.
S. S. Harakannanavar, J. M. Rudagi, V. I. Puranikmath, A. Siddiqua, and R. Pramodhini, “Plant leaf disease detection using computer vision and machine learning algorithms,” Glob. Transitions Proc., vol. 3, no. 1, pp. 305–310, 2022, doi: 10.1016/j.gltp.2022.03.016.
T. S. Xian and R. Ngadiran, “Plant Diseases Classification using Machine Learning,” J. Phys. Conf. Ser., vol. 1962, no. 1, 2021, doi: 10.1088/1742-6596/1962/1/012024.
A. Susanto, Z. H. Dewantoro, C. A. Sari, D. R. I. M. Setiadi, E. H. Rachmawanto, and I. U. W. Mulyono, “Shallot Quality Classi-fication using HSV Color Models and Size Identification based on Naive Bayes Classifier,” J. Phys. Conf. Ser., vol. 1577, no. 1, 2020, doi: 10.1088/1742-6596/1577/1/012020.
O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, “Tomatoes classification using K-NN based on GLCM and HSV color space,” in Proceedings - 2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017, Nov. 2018, vol. 2018-Janua, pp. 1–6. doi: 10.1109/INNOCIT.2017.8319133.
D. Fajri Riesaputri, C. Atika Sari, I. M. S. De Rosal, and E. Hari Rachmawanto, “Classification of Breast Cancer using PNN Classifier based on GLCM Feature Extraction and GMM Segmentation,” in 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2020, pp. 83–87. doi: 10.1109/iSemantic50169.2020.9234207.
E. Hari Rachmawanto, G. Rambu Anarqi, D. R. I. Moses Setiadi, and C. Atika Sari, “Handwriting Recognition Using Eccentricity and Metric Feature Extraction Based on K-Nearest Neighbors,” in Proceedings - 2018 International Seminar on Application for Technology of Information and Communication: Creative Technology for Human Life, iSemantic 2018, Nov. 2018, pp. 411–416. doi: 10.1109/ISEMANTIC.2018.8549804.
J. Heaton, “Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning,” Genet. Program. Evolvable Mach., vol. 19, no. 1–2, pp. 305–307, Jun. 2018, doi: 10.1007/s10710-017-9314-z.
A. Susanto, I. U. Wahyu Mulyono, C. Atika Sari, E. Hari Rachmawanto, D. R. I. M. Setiadi, and M. K. Sarker, “Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation,” IAES Int. J. Artif. Intell., vol. 12, no. 3, p. 1448, Sep. 2023, doi: 10.11591/ijai.v12.i3.pp1448-1458.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.
T. Liu, P. Zheng, and J. Bao, “Deep learning-based welding image recognition: A comprehensive review,” J. Manuf. Syst., vol. 68, no. March, pp. 601–625, Jun. 2023, doi: 10.1016/j.jmsy.2023.05.026.
V. Singh, A. Chug, and A. P. Singh, “Classification of Beans Leaf Diseases using Fine Tuned CNN Model,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 348–356, 2023, doi: 10.1016/j.procs.2023.01.017.
S. Verma, P. Kumar, and J. P. Singh, “A meta-learning framework for recommending CNN models for plant disease identification tasks,” Comput. Electron. Agric., vol. 207, no. June 2022, p. 107708, 2023, doi: 10.1016/j.compag.2023.107708.
M. T. Ahad, Y. Li, B. Song, and T. Bhuiyan, “Comparison of CNN-based deep learning architectures for rice diseases classification,” Artif. Intell. Agric., Jul. 2023, doi: 10.1016/j.aiia.2023.07.001.
S. M. Hassan, A. K. Maji, M. Jasi?ski, Z. Leonowicz, and E. Jasi?ska, “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach,” Electronics, vol. 10, no. 12, p. 1388, Jun. 2021, doi: 10.3390/electronics10121388.
K. Dong, C. Zhou, Y. Ruan, and Y. Li, “MobileNetV2 Model for Image Classification,” Proc. - 2020 2nd Int. Conf. Inf. Technol. Comput. Appl. ITCA 2020, pp. 476–480, Dec. 2020, doi: 10.1109/ITCA52113.2020.00106.
R. Indraswari, R. Rokhana, and W. Herulambang, “Melanoma image classification based on MobileNetV2 network,” Procedia Comput. Sci., vol. 197, pp. 198–207, Jan. 2022, doi: 10.1016/J.PROCS.2021.12.132.
C. Wang et al., “Pulmonary image classification based on inception-v3 transfer learning model,” IEEE Access, vol. 7, pp. 146533–146541, 2019, doi: 10.1109/ACCESS.2019.2946000.
K. Joshi, V. Tripathi, C. Bose, and C. Bhardwaj, “Robust Sports Image Classification Using InceptionV3 and Neural Networks,” Procedia Comput. Sci., vol. 167, pp. 2374–2381, Jan. 2020, doi: 10.1016/J.PROCS.2020.03.290.
L. Fu, S. Li, Y. Sun, Y. Mu, T. Hu, and H. Gong, “Lightweight-Convolutional Neural Network for Apple Leaf Disease Identifica-tion,” Front. Plant Sci., vol. 13, no. May, pp. 1–10, 2022, doi: 10.3389/fpls.2022.831219.
S. Bhattarai, “New Plant Diseases Dataset,” Kaggle, 2018. https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
DOI: https://doi.org/10.33633/jcta.v1i1.8877
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