Classification bootstrap resnet50 for potato diseases datasets
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Published:
Feb 28, 2024
Keywords:
Accuracy, Potato diseases, Bootstrap, ResNet50, Classification
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Computer Science
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Abstract
Potato diseases can have negative impacts on crop production, food security, income generation, trade competitiveness, consumer health, environmental sustainability, etc. Therefore, it is important to prevent, monitor, diagnose, treat, and control potato diseases using various methods such as cultural practices (e.g., crop rotation, intercropping), biological control (e.g., beneficial insects), chemical control (e.g., fungicides), physical control (e.g., pruning), etc.Classification Bootstrap ResNet50 is a topic related to image classification using a deep learning model called ResNet50. ResNet50 is a convolutional neural network that has 50 layers and can classify images into 1000 object categories, such as animals, plants, vehicles, etc. ResNet50 is based on the concept of residual learning, which means that it uses skip connections to avoid the problem of vanishing gradients and improve the performance of the network. we used classification bootstrap and ResNet50 to analyze a dataset of potato images with different diseases. We compared the performance of ResNet50 with other models, such as Inception-v3 and Xception, and used bootstrap to evaluate the accuracy and variability of the models. We also used data augmentation and dropout techniques to optimize the models and prevent overfitting. We define some functions to run training by doing Early Stop, train_steps, val_steps, predictions. In the research I conducted, I got the highest accuracy results, reaching 100% accuracy by testing epochs with a total of 0-41 epochs.
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Simbolon, H. F. S. (2024) “Classification bootstrap resnet50 for potato diseases datasets”, Jurnal Mantik, 7(4), pp. 3334-3341. doi: 10.35335/mantik.v7i4.4587.
References
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Islam, F., Hoq, M.N., Rahman, C.M. (2019). Application of transfer learning to detect potato disease from leaf image. In: IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), pp. 127–130. IEEE.
Jasim, M.A., Al-Tuwaijari, J.M. (2020). Plant leaf diseases detection and classification using image processing and deep learning techniques. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), pp. 259–265. IEEE.
Jin W, Yane Li, Hailin F, Ren Lijin Du, Jian XW. (2020). Common pests image recognition based on deep convolutional neural network. Comput Electron Agric 179(1):105834. https://doi.org/10.1016/j.compag.2020.105834
Kashyap CK, Rashmi M, Chandan C, Kangkana B. (2021). Automated recognition of optical image based potato leaf blight diseases using deep learning - ScienceDirect. Physiol Mol Plant Pathol 117:101781. https://doi.org/10.1016/J.PMPP.2021.101781
Nashrullah, Faiz., Wibowo, Suryo Adhi., Budiman, Gelar. (2020). Investigasi Parameter Epoch Pada Arsitektur ResNet50 Untuk Klasifikasi Pornografi. Journal of Computer, Electronic, and Telecommunication ISSN 2723-4371, E-ISSN 2723-5912 doi.org/10.52435/complete.v1i1.51
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Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A. (2019). Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J. Photogramm. Remote. Sens. 152, 166–177.
Mora-Romero, G.A., Félix-Gastélum, R., Bomberger, R.A. et al. (2022). Common potato disease symptoms: ambiguity of symptom-based identification of causal pathogens and value of on-site molecular diagnostics. J Gen Plant Pathol 88, 89–104. https://doi.org/10.1007/s10327-021-01045-2
Mukti, I.Z., Biswas, D. (2019). Transfer learning based plant diseases detection using ResNet50. In: 4th International Conference on Electrical Information and Communication Technology (EICT), pp. 1–6. IEEE.
R. Ferjaoui, M. A. Cherni, F. Abidi and A. Zidi. (2022). Deep Residual Learning based on ResNet50 for COVID-19 Recognition in Lung CT Images. 8th International Conference on Control, Decision and Information Technologies (CoDIT), Istanbul, Turkey, pp. 407-412, doi: 10.1109/CoDIT55151.2022.9804094.
R. Kumar, D. Singh, A. Chug and A. P. Singh. (2022). Evaluation of Deep learning based Resnet-50 for Plant Disease Classification with Stability Analysis. 6th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 1280-1287, doi: 10.1109/ICICCS53718.2022.9788207.
Sanjay A. Agrawal., Vaibhav D., Rewaskar., Rucha A., Agrawal., Swapnil S. Chaudhari., Yogendra Patil., Nidhee S. Agrawal. (2023). Advancements in Nsfw Content Detection: A Comprehensive Review of Resnet-50 Based Approaches. International Journal Ofintelligent Systems and Applications In Engineering.
Shabbir, A., et al. (2021). Satellite and scene image classification based on transfer learning and fine tuning of resnet50. Math. Probl. Eng. 2021, 1–18.
Suherman, Endang., Rahman, Ben., Hindarto, Djarot., Santoso, Handri. (2023). Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects. Sinkron : Jurnal dan Penelitian Teknik Informatika Volume 8, Number 2.
Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., Bhardwaj, S. (2020). Potato leaf diseases detection using deep learning. In:4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 461–466. IEEE.
Too, E. C. Yujian, L. Njuki, S. Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric.
Umamaheswari S; Vishal N R; Pragadesh N R; Lavanya S. (2023). Performance Analysis of ResNet50 Architecture based Pest Detection System. 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023, pp. 578-583, doi: 10.1109/ICACCS57279.2023.10112802.
Arshad, M.S., Rehman, U.A., Fraz, M.M. (2021). Plant disease identification using transfer learning. In: 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1–5. IEEE.
Bhakta I, Phadikar S, Majumder K et al. (2023). A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agric 24(1):23–39
Chen, J. Zhang, D. Nanehkaran, Y. A. (2020). Identifying plant diseases using deep transfer learning and enhanced lightweight network. https://doi.org/10.1007/s11042-020-09669-w
Dasgupta, S. R. Rakshit, S. Mondal, D. Kole, D. K. (2020). Detection of diseases in potato leaves using transfer learning. Computational Intelligence in Pattern Recognition Springer Singapore
Fauzan Muhammad., Aniati Murni Arimurthy., Dina Chahyati. (2023). Transfer Learningdengan Metode Fine Tuning pada Model NetworkVGG16 dan ResNet50. Indonesian Journal of Computer Science.
Islam, F., Hoq, M.N., Rahman, C.M. (2019). Application of transfer learning to detect potato disease from leaf image. In: IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), pp. 127–130. IEEE.
Jasim, M.A., Al-Tuwaijari, J.M. (2020). Plant leaf diseases detection and classification using image processing and deep learning techniques. In: 2020 International Conference on Computer Science and Software Engineering (CSASE), pp. 259–265. IEEE.
Jin W, Yane Li, Hailin F, Ren Lijin Du, Jian XW. (2020). Common pests image recognition based on deep convolutional neural network. Comput Electron Agric 179(1):105834. https://doi.org/10.1016/j.compag.2020.105834
Kashyap CK, Rashmi M, Chandan C, Kangkana B. (2021). Automated recognition of optical image based potato leaf blight diseases using deep learning - ScienceDirect. Physiol Mol Plant Pathol 117:101781. https://doi.org/10.1016/J.PMPP.2021.101781
Nashrullah, Faiz., Wibowo, Suryo Adhi., Budiman, Gelar. (2020). Investigasi Parameter Epoch Pada Arsitektur ResNet50 Untuk Klasifikasi Pornografi. Journal of Computer, Electronic, and Telecommunication ISSN 2723-4371, E-ISSN 2723-5912 doi.org/10.52435/complete.v1i1.51
Li, Weiming, Siqi Yu, Runhuang Yang, Yixing Tian, Tianyu Zhu, Haotian Liu, Danyang Jiao, Feng Zhang, Xiangtong Liu, Lixin Tao, and et al. (2023). Machine Learning Model of ResNet50-Ensemble Voting for Malignant–Benign Small Pulmonary Nodule Classification on Computed Tomography Images. Cancers 15, no. 22: 5417. https://doi.org/10.3390/cancers15225417
Liu, Y., Chen, X., Wang, Z., Wang, Z.J., Ward, R.K., Wang, X. (2018). Deep learning for pixel-level image fusion: recent advances and future prospects. Information Fusion 42, 158–173.
Liyuan Wang., Yulong Chen., Xiaoye Wang., Ruixing Wang., Hao Chen., Yinhai Zhu. (2023). Research on Remote Sensing Image Classification Based on Transfer Learning and Data Augmentation. International Conference on Knowledge Science, Engineering and Management KSEM 2023: Knowledge Science, Engineering and Management pp 99–111
Lv, Z., Hu, Y., Zhong, H., Wu, J., Li, B., Zhao, H. (2018). Parallel K-means clustering of remote sensing images based on mapReduce. In: Wang, F.L., Gong, Z., Luo, X., Lei, J. (eds.) WISM. LNCS, vol. 6318, pp. 162–170. Springer, Heidelberg. https://doi.org/10.1007/978-3-642-16515-3_21
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A. (2019). Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J. Photogramm. Remote. Sens. 152, 166–177.
Mora-Romero, G.A., Félix-Gastélum, R., Bomberger, R.A. et al. (2022). Common potato disease symptoms: ambiguity of symptom-based identification of causal pathogens and value of on-site molecular diagnostics. J Gen Plant Pathol 88, 89–104. https://doi.org/10.1007/s10327-021-01045-2
Mukti, I.Z., Biswas, D. (2019). Transfer learning based plant diseases detection using ResNet50. In: 4th International Conference on Electrical Information and Communication Technology (EICT), pp. 1–6. IEEE.
R. Ferjaoui, M. A. Cherni, F. Abidi and A. Zidi. (2022). Deep Residual Learning based on ResNet50 for COVID-19 Recognition in Lung CT Images. 8th International Conference on Control, Decision and Information Technologies (CoDIT), Istanbul, Turkey, pp. 407-412, doi: 10.1109/CoDIT55151.2022.9804094.
R. Kumar, D. Singh, A. Chug and A. P. Singh. (2022). Evaluation of Deep learning based Resnet-50 for Plant Disease Classification with Stability Analysis. 6th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 1280-1287, doi: 10.1109/ICICCS53718.2022.9788207.
Sanjay A. Agrawal., Vaibhav D., Rewaskar., Rucha A., Agrawal., Swapnil S. Chaudhari., Yogendra Patil., Nidhee S. Agrawal. (2023). Advancements in Nsfw Content Detection: A Comprehensive Review of Resnet-50 Based Approaches. International Journal Ofintelligent Systems and Applications In Engineering.
Shabbir, A., et al. (2021). Satellite and scene image classification based on transfer learning and fine tuning of resnet50. Math. Probl. Eng. 2021, 1–18.
Suherman, Endang., Rahman, Ben., Hindarto, Djarot., Santoso, Handri. (2023). Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects. Sinkron : Jurnal dan Penelitian Teknik Informatika Volume 8, Number 2.
Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., Bhardwaj, S. (2020). Potato leaf diseases detection using deep learning. In:4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 461–466. IEEE.
Too, E. C. Yujian, L. Njuki, S. Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric.
Umamaheswari S; Vishal N R; Pragadesh N R; Lavanya S. (2023). Performance Analysis of ResNet50 Architecture based Pest Detection System. 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023, pp. 578-583, doi: 10.1109/ICACCS57279.2023.10112802.
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