Comparison of Convolutional Neural Network Methods for the Classification of Maize Plant Diseases
DOI:
https://doi.org/10.26594/register.v10i1.3656Keywords:
corn disease classification, Convolutional Neural Network, Image Analysis Algorithms, Disease Detection Accuracy, Agricultural Technology InnovationAbstract
The focus of this study is the classification of maize images with common rust, gray leaf spot, blight, and healthy diseases. Various models, including ResNet50, ResNet101, Xception, VGG16, and ENet, were tested for this purpose. The dataset used for corn plant diseases is publicly available, and the data were split into separate sets for training, validation, and testing. After processing the data, the following models were identified: the Xception model epoch with an accuracy of 83.74%, the ResNet model with an accuracy of 97.19% at epoch 8/10, the ResNet101 model with an accuracy of 97.55% at epoch 10/10, and the ENet model with an accuracy of 98.69% at epoch 9/1000. ENet exhibited the highest accuracy among the five models at 98.69%. Additionally, ENet achieved an average accuracy of 95.45%, the highest among all tested models, based on the average accuracy in the confusion matrix. This research indicates that ENet performs best at processing data related to maize plant diseases. Consequently, the analysis of maize plant diseases is expected to evolve as a result of this research. Following the implementation of the system's generated model, this research will continue to explore its impact. The intention is to provide a summary of the comparative classification performance of CNN algorithms.
References
J. Gupta, S. Pathak, and G. Kumar, "Deep Learning (CNN) and Transfer Learning: A Review," J. Phys. Conf. Ser., vol. 2273, no. 1, 2022, doi: 10.1088/1742-6596/2273/1/012029.
N. Sharma, V. Jain, and A. Mishra, "An analysis of convolutional neural networks for image classification," Procedia Comput. Sci., 2018, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050918309335
Y. Li, J. Nie, and X. Chao, "Do we really need deep CNN for plant diseases identification?," Comput. Electron. Agric., vol. 178, Nov. 2020, doi: 10.1016/j.compag.2020.105803.
P. Harsan, A. Qurania, and K. Damayanti, "Maize Plant Desease Identification (Zea Mays L. Saccharata) Using Image Processing and K-Nearest Neighbor (K-Nn)," Int. J. Eng. Technol., vol. 7, no. 3.20, p. 402, 2018, doi: 10.14419/ijet.v7i3.20.20581.
A. Hidayat, U. Darusalam, and I. Irmawati, "Detection of Disease on Corn Plants Using Convolutional Neural Network Methods," J. Ilmu Komput. dan …, 2019, [Online]. Available: http://jiki.cs.ui.ac.id/index.php/jiki/article/view/695
A. Wu et al., "Classification of corn kernels grades using image analysis and support vector machine," Adv. Mech. Eng., vol. 10, no. 12, pp. 1–9, 2018, doi: 10.1177/1687814018817642.
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, no. February 2019, pp. 93–97, 2019, doi: 10.1109/IC3INA.2018.8629507.
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, doi: 10.12928/TELKOMNIKA.v18i3.14840.
F. Rajeena P. P, A. S. U, M. A. Moustafa, and M. A. S. Ali, "Detecting Plant Disease in Corn Leaf Using EfficientNet Architecture—An Analytical Approach," Electron., vol. 12, no. 8, 2023, doi: 10.3390/electronics12081938.
Y. Chen et al., "DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification," Agric., vol. 12, no. 12, 2022, doi: 10.3390/agriculture12122047.
M. Brahimi, K. Boukhalfa, and A. Moussaoui, "Deep Learning for Tomato Diseases: Classification and Symptoms Visualization," Appl. Artif. Intell., vol. 31, no. 4, pp. 299–315, 2017, doi: 10.1080/08839514.2017.1315516.
A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, "Deep learning for image-based cassava disease detection," Front. Plant Sci., vol. 8, no. October, pp. 1–7, 2017, doi: 10.3389/fpls.2017.01852.
A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, "A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition," Sensors (Switzerland), vol. 17, no. 9, 2017, doi: 10.3390/s17092022.
J. Amara, B. Bouaziz, and A. Algergawy, "A deep learning-based approach for banana leaf diseases classification," Lect. Notes Informatics (LNI), Proc. - Ser. Gesellschaft fur Inform., vol. 266, pp. 79–88, 2017.
P. Lacerda, B. Barros, C. Albuquerque, and A. Conci, "Hyperparameter optimization for COVID-19 pneumonia diagnosis based on chest CT," Sensors, vol. 21, no. 6, pp. 1–11, 2021, doi: 10.3390/s21062174.
A. Rama, M. Bhavani, and V. Surya, "Hyper Parameter Tuning of Pre-Trained Deep Learning Model for an Efficient Medical Image Classification Using Cnn," J. Crit. Rev., no. September, 2020, doi: 10.13140/RG.2.2.28985.39525.
A. E. Minarno, M. Hazmi Cokro Mandiri, Y. Munarko, and H. Hariyady, "Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification," Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, 2021, doi: 10.22219/kinetik.v6i2.1219.
D. Motta et al., "Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes," PLoS One, vol. 15, no. 7, pp. 1–30, 2020, doi: 10.1371/journal.pone.0234959.
R. Agarwal and H. Sharma, "Enhanced Convolutional Neural Network (ECNN) for Maize Leaf Diseases Identification," Smart Innov. Commun. Comput. Sci., 2021, [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-15-5345-5_27
Z. Cao, S. Mu, and M. Dong, "Two-attribute e-commerce image classification based on a convolutional neural network," Vis. Comput., 2020, doi: 10.1007/s00371-019-01763-x.
I. M. Adekunle, "Implementation of Improved Machine Learning Techniques for Plant Disease Detection and Classification," Int. J. Res. Innov. Appl. Sci. |, vol. V, no. Vi, pp. 2454–6194, 2020, [Online]. Available: www.rsisinternational.org
A. Taslim, S. Saon, A. K. Mahamad, M. Muladi, and W. N. Hidayat, "Plant leaf identification system using convolutional neural network," Bull. Electr. Eng. Informatics, vol. 10, no. 6, pp. 3341–3352, 2021, doi: 10.11591/eei.v10i6.2332.
S. Jasrotia, J. Yadav, N. Rajpal, M. Arora, and J. Chaudhary, "Convolutional Neural Network Based Maize Plant Disease Identification," Procedia Comput. Sci., vol. 218, no. 2022, pp. 1712–1721, 2022, doi: 10.1016/j.procs.2023.01.149.
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification," Comput. Intell. Neurosci., vol. 2016, 2016, doi: 10.1155/2016/3289801.
H. Qi, Y. Liang, Q. Ding, and J. Zou, "Automatic identification of peanut-leaf diseases based on stack ensemble," Appl. Sci., vol. 11, no. 4, pp. 1–15, 2021, doi: 10.3390/app11041950.
A. Bashar, "Survey on evolving deep learning neural network architectures," Journal of Artificial Intelligence. irojournals.com, 2019. [Online]. Available: https://www.irojournals.com/aicn/V1/I2/03.pdf
G. Wang, Y. Sun, and J. Wang, "Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning," Comput. Intell. Neurosci., vol. 2017, 2017, doi: 10.1155/2017/2917536.
S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using deep learning for image-based plant disease detection," Front. Plant Sci., vol. 7, no. September, pp. 1–10, 2016, doi: 10.3389/fpls.2016.01419.
X. Xie, X. Zhang, B. He, D. Liang, D. Zhang, and L. Huang, "A system for diagnosis of wheat leaf diseases based on Android smartphone," Opt. Meas. Technol. Instrum., vol. 10155, p. 1015526, 2016, doi: 10.1117/12.2246919.
J. Liu and X. Wang, "Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network," Front. Plant Sci., vol. 11, no. June, pp. 1–12, 2020, doi: 10.3389/fpls.2020.00898.
X. Xie, Y. Ma, B. Liu, J. He, S. Li, and H. Wang, "A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks," Front. Plant Sci., vol. 11, no. June, pp. 1–14, 2020, doi: 10.3389/fpls.2020.00751.
Y. Guo et al., "Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming," Discret. Dyn. Nat. Soc., vol. 2020, 2020, doi: 10.1155/2020/2479172.
A. Jayakumar, "Detection and Classification of Leaf Diseases in Maize Plant using Machine Learning," 2020, [Online]. Available: http://trap.ncirl.ie/id/eprint/4278
A. Nawrocka, M. Nawrocki, and A. Kot, "Research study of image classification algorithms based on Convolutional Neural Networks," Proc. 2023 24th Int. Carpathian Control Conf. ICCC 2023, pp. 299–302, 2023, doi: 10.1109/ICCC57093.2023.10178933.
W. Y. Lee, S. M. Park, and K. B. Sim, "Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm," Optik (Stuttg)., vol. 172, no. May, pp. 359–367, 2018, doi: 10.1016/j.ijleo.2018.07.044.
K. R. Prilianti, T. H. P. Brotosudarmo, S. Anam, and A. Suryanto, "Performance comparison of the convolutional neural network optimizer for photosynthetic pigments prediction on plant digital image," AIP Conf. Proc., vol. 2084, no. March, 2019, doi: 10.1063/1.5094284.
J. Park, D. I. Kim, B. Choi, W. Kang, and H. W. Kwon, "Classification and Morphological Analysis of Vector Mosquitoes using Deep Convolutional Neural Networks," Sci. Rep., vol. 10, no. 1, pp. 1–12, 2020, doi: 10.1038/s41598-020-57875-1.
R. C. Gonzales and R. E. Woods, Digital Image Processing. New Jersey: Prentice-Hall inc, 2002.
A. E. Minarno, Y. Sasongko, Y. Munarko, H. A. Nugroho, and Z. Ibrahim, "INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION journal homepage : www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION Convolutional Neural Network featuring VGG-16 Model for Glioma Classification.” [Online]. Available: www.joiv.org/index.php/joiv
M. S. A. M. Al-Gaashani, N. A. Samee, R. Alnashwan, M. Khayyat, and M. S. A. Muthanna, "Using a Resnet50 with a Kernel Attention Mechanism for Rice Disease Diagnosis," Life, vol. 13, no. 6, pp. 1-14, 2023, doi: 10.3390/life13061277.
Z. Xu, K. Sun, and J. Mao, "Research on ResNet101 Network Chemical Reagent Label Image Classification Based on Transfer Learning," in Proceedings of 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 354–358. doi: 10.1109/ICCASIT50869.2020.9368658.
K. Shaheed, Q. Abbas, A. Hussain, and I. Qureshi, "Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images," Diagnostics, vol. 13, no. 15, Aug. 2023, doi: 10.3390/diagnostics13152583.
J. Cai et al., "Improved EfficientNet for corn disease identification," Front. Plant Sci., vol. 14, no. September, pp. 1–17, 2023, doi: 10.3389/fpls.2023.1224385.
H. Wu et al., "Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery," Plant Phenome J., vol. 2, no. 1, pp. 1–9, 2019, doi: 10.2135/tppj2019.03.0006.
M. Saha and E. Sasikala, "Identification of Plants leaf Diseases using Machine Learning Algorithms," Int. J. Adv. Sci. Technol., vol. 29, no. 9, pp. 2900–2910, 2020.
D. Bhatt et al., "Cnn variants for computer vision: History, architecture, application, challenges and future scope," Electronics (Switzerland), vol. 10, no. 20. MDPI, Oct. 01, 2021. doi: 10.3390/electronics10202470.
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
Q. V. Le Mingxing Tan, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing," Can. J. Emerg. Med., vol. 15, no. 3, p. 190, 2013.
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