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Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification
Corresponding Author(s) : Riries Rulaningtyas
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 6, No. 4, November 2021
Abstract
Maize productivity growth is expected to increase by the year. However, there are obstacles to achieving it. One of the causes is diseases attack. Generally, maize plant diseases are easily detected through the leaves. This article discusses maize leaf disease classification using computer vision with a convolutional neural network (CNN). It aims to compare the deep convolutional neural network (CNN) AlexNet and Squeezenet. The network also used optimization, stochastic gradient descent with momentum (SGDM). The dataset for this experiment was taken from PlantVillage with 3852 images with 4 classes i.e healthy, blight, spot, and rust. The data is divided into 3 parts: training, validation, and testing. Training and validation are 80%, the rest for testing. The results of training with cross-validation produce the best accuracy of 100% for AlexNet and Squeezenet. Furthermore, the best weights and biases are stored in the model for testing data classification. The recognition results using AlexNet showed 97.69% accuracy. While the results of Squeezenet 44.49% accuracy. From this experiment environment, it can be concluded that AlexNet is better than Squeezenet for maize leaf diseases classification.
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- Kementrian Pertanian, “Produktivitas jagung menurut Provinsi 2014-2018,” 2019.
- Kementrian Pertanian, “Produksi jagung menurut Provinsi 2014-2018,” Kementrian Pertanian, 2019.
- M. S. Sudjono, “Penyakit Jagung dan Pengendaliannya,” 2015.
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- F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” in ICLR, 2017, pp. 1–13. https://doi.org/10.1007/978-3-319-24553-9
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” pp. 1–9, 2012. https://doi.org/10.1145/3065386
- S. Ruder, “An overview of gradient descent optimization,” pp. 1–14, 2017.
- A. Ramezani-Kebrya, A. Khisti, and B. Liang, “On the Generalization of Stochastic Gradient Descent with Momentum,” no. 2015, pp. 1–36, 2021.
- J. Arun Pandian And G. GeetharamanI, “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1,” Mendeley, 2019.
- D. Berrar, “Cross-validation,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, no. April, pp. 542–545, 2018. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
- S. Raschka, “Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,” 2018.
- Q. H. Nguyen et al., “Influence of data splitting on performance of machine learning models in prediction of shear strength of soil,” Math. Probl. Eng., vol. 2021, no. February, 2021. https://doi.org/10.1155/2021/4832864
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- A. Ingle, “Plant Diseases Classification using ResNet-9,” Kaggle, 2019.
- Milan, “Maize Disease using VGG16 and ADAM,” Kaggle, 2019.
- Z. Ren, V. Pandit, K. Qian, and Z. Yang, “Deep Sequential Image Features on Acoustic Scene Classification,” in Detection and Classification of Acoustic Scenes and Events, 2017, no. November, pp. 1–6.
- L. N. Smith, “A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay,” pp. 1–21, 2018.
- W. Setiawan, M. . Utoyo, and R. Rulaningtyas, “Classification of neovascularization using convolutional neural network model,” TELKOMNIKA, vol. 17, no. 1, pp. 463–473, 2019. https://doi.org/10.12928/TELKOMNIKA.v17i1.11604
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- R. Acharya, “Corn Leaf Infection Dataset, Version 1,” Kaggle, 2020.
- K. Aurangzeb, F. Akmal, M. Khan, Muhammad Attique Sharif, and M. Y. Javed, “Advanced machine learning algorithm based system for crops leaf diseases recognition,” in 6th Conference on Data Science and Machine Learning Applications (CDMA), 2020. https://doi.org/10.1109/CDMA47397.2020.00031
- J. Wang and L. Perez, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” 2017.
- D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in ICLR, 2015, pp. 1–15. http://doi.acm.org.ezproxy.lib.ucf.edu/10.1145/1830483.1830503
References
Kementrian Pertanian, “Produktivitas jagung menurut Provinsi 2014-2018,” 2019.
Kementrian Pertanian, “Produksi jagung menurut Provinsi 2014-2018,” Kementrian Pertanian, 2019.
M. S. Sudjono, “Penyakit Jagung dan Pengendaliannya,” 2015.
M. D. Chauhan, R. Walia, C. Singh, and M. Deivakani, “Detection of Maize Disease Using Random Forest Classification Algorithm,” vol. 12, no. 9, pp. 715–720, 2021. https://doi.org/10.17762/turcomat.v12i9.3141
F. Lin, D. Zhang, Y. Huang, X. Wang, and X. Chen, “Detection of corn and weed species by the combination of spectral, shape and textural features,” Sustain., vol. 9, no. 8, pp. 1–14, 2017. https://doi.org/10.3390/su9081335
B. S. Kusumo, A. Heryana, O. Mahendra, and H. F. Pardede, “Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing,” 2018 Int. Conf. Comput. Control. Informatics its Appl. Recent Challenges Mach. Learn. Comput. Appl. IC3INA 2018 - Proceeding, pp. 93–97, 2019. https:// doi.org/10.1109/IC3INA.2018.8629507
R. Meng et al., “Development of spectral disease indices for southern corn rust detection and severity classification,” Remote Sens., vol. 12, no. 19, pp. 1–16, 2020. https://doi.org/10.3390/rs12193233
Y. Wei, L. Wei, T. Ji, and H. Hu, “A Novel Image Classification Approach for Maize Diseases Recognition,” Recent Adv. Electr. Electron. Eng., vol. 13, no. 3, pp. 331–339, 2020. https://doi.org/10.2174/2352096511666181003134208
M. Syarief and W. Setiawan, “Convolutional neural network for maize leaf disease image classification,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 3, pp. 1376–1381, 2020. http://dx.doi.org/10.12928/telkomnika.v18i3.14840
E. L. Da Rocha, L. Rodrigues, and J. F. Mari, “Maize leaf disease classification using convolutional neural networks and hyperparameter optimization,” pp. 104–110, 2021. https://doi.org/10.5753/wvc.2020.13489
X. Sun and J. Wei, “Identification of maize disease based on transfer learning,” J. Phys. Conf. Ser., vol. 1437, no. 1, 2020. https://doi.org/10.1088/1742-6596/1437/1/012080
A. Waheed, M. Goyal, D. Gupta, A. Khanna, A. E. Hassanien, and H. M. Pandey, “An optimized dense convolutional neural network model for disease recognition and classification in corn leaf,” Comput. Electron. Agric., vol. 175, pp. 678–683, 2020. https://doi.org/10.1016/j.compag.2020.105456.
D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, “PlantDoc: A dataset for visual plant disease detection,” ACM Int. Conf. Proceeding Ser., no. January, pp. 249–253, 2020. https://doi.org/10.1145/3371158.3371196
M. M. Micheni, M. Kinyua, B. Too, and C. Gakii, “Maize Leaf Disease Detection using Convolutional Neural Networks,” J. Appl. Comput. Sci. Math., vol. 15, no. 1, pp. 15–20, 2021. https://doi.org/10.4316/JACSM.202101002
Y. Xu, B. Zhao, Y. Zhai, Q. Chen, and Y. Zhou, “Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network,” IEEE Access, vol. 9, pp. 27959–27970, 2021. https://doi.org/10.1109/ACCESS.2021.3058267
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” in ICLR, 2017, pp. 1–13. https://doi.org/10.1007/978-3-319-24553-9
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” pp. 1–9, 2012. https://doi.org/10.1145/3065386
S. Ruder, “An overview of gradient descent optimization,” pp. 1–14, 2017.
A. Ramezani-Kebrya, A. Khisti, and B. Liang, “On the Generalization of Stochastic Gradient Descent with Momentum,” no. 2015, pp. 1–36, 2021.
J. Arun Pandian And G. GeetharamanI, “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1,” Mendeley, 2019.
D. Berrar, “Cross-validation,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, no. April, pp. 542–545, 2018. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
S. Raschka, “Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,” 2018.
Q. H. Nguyen et al., “Influence of data splitting on performance of machine learning models in prediction of shear strength of soil,” Math. Probl. Eng., vol. 2021, no. February, 2021. https://doi.org/10.1155/2021/4832864
A. Rácz, D. Bajusz, and K. Héberger, “Effect of dataset size and train/test split ratios in qsar/qspr multiclass classification,” Molecules, vol. 26, no. 4, pp. 1–16, 2021. https://doi.org/10.3390/molecules26041111
A. Ingle, “Plant Diseases Classification using ResNet-9,” Kaggle, 2019.
Milan, “Maize Disease using VGG16 and ADAM,” Kaggle, 2019.
Z. Ren, V. Pandit, K. Qian, and Z. Yang, “Deep Sequential Image Features on Acoustic Scene Classification,” in Detection and Classification of Acoustic Scenes and Events, 2017, no. November, pp. 1–6.
L. N. Smith, “A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay,” pp. 1–21, 2018.
W. Setiawan, M. . Utoyo, and R. Rulaningtyas, “Classification of neovascularization using convolutional neural network model,” TELKOMNIKA, vol. 17, no. 1, pp. 463–473, 2019. https://doi.org/10.12928/TELKOMNIKA.v17i1.11604
W. Setiawan and F. Damayanti, “Layers Modification of Convolutional Neural Network for Pneumonia Detection,” in Journal of Physics: Conference Series, 2020, vol. 1477, no. 5, pp. 1–9. https://doi.org/10.1088/1742-6596/1477/5/052055
R. Acharya, “Corn Leaf Infection Dataset, Version 1,” Kaggle, 2020.
K. Aurangzeb, F. Akmal, M. Khan, Muhammad Attique Sharif, and M. Y. Javed, “Advanced machine learning algorithm based system for crops leaf diseases recognition,” in 6th Conference on Data Science and Machine Learning Applications (CDMA), 2020. https://doi.org/10.1109/CDMA47397.2020.00031
J. Wang and L. Perez, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” 2017.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in ICLR, 2015, pp. 1–15. http://doi.acm.org.ezproxy.lib.ucf.edu/10.1145/1830483.1830503