Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling





Convolutional Neural Network, Deep Learning , ResNet50, Rice Diseases, Support Vector Machine SVM.


The rice crop is one of the most important food crops that depend on it globally. Therefore, farmers must preserve the production of this crop from infection with pests and diseases that lead to its destruction through artificial intelligence and deep learning techniques. A hybrid model combining a Residual Network 50 (ResNet50) deep convolutional neural network (CNN) and a support vector machine (SVM) developed diagnoses rice diseases. Farmers or people working in agriculture could use this model to quickly and accurately identify the diseases in their crops and treat them, increasing crop yield and reducing the need for costly and time-consuming manual inspection. ResNet50, a deep learning model effective at image classification tasks, was used to extract features from images of rice plants. SVM was then used to classify the diseases based on these features. The ResNet50 was able to capture complex patterns in the images, while the SVM was able to use these patterns to make accurate classification decisions. This hybrid model allowed for high precision in rice disease diagnosis, achieving an accuracy of approximately 99%.


Download data is not yet available.


C. Calpe, Rice in world trade, Part II. Status of the world rice market, Proc. 20 Th Sess. Int. Rice Comm. (2002).

P. Varma, P. Varma, Ghosh, Rice productivity and food security in India, Springer, 2017. DOI: https://doi.org/10.1007/978-981-10-3692-7

M.K. Papademetriou, Rice production in the Asia-Pacific Region: Issues and perspectives. In’Bridging the Rice Yield Gap in the Asia-Pacific Region’. FAO, UN, Bangkok, Thailand, RAP Publ. 16 (2000) 2000.

Y. Tang, Deep learning using linear support vector machines, ArXiv Prepr. ArXiv1306.0239. (2013).

A.S.B. Reddy, D.S. Juliet, Transfer learning with ResNet-50 for malaria cell-image classification, in: 2019 Int. Conf. Commun. Signal Process., IEEE, 2019:pp.945–949.http://doi.org/10.1109/ICCSP.2019.8697909. DOI: https://doi.org/10.1109/ICCSP.2019.8697909

S. Almabdy, L. Elrefaei, Deep convolutional neural network-based approaches for face recognition, Appl. Sci. 9(2019)4397. https://doi.org/10.3390/app9204397 DOI: https://doi.org/10.3390/app9204397

F. Jiang, Y. Lu, Y. Chen, D. Cai, G. Li, Image recognition of four rice leaf diseases based on deep learning and support vector machine, Comput. Electron. Agric. 179 (2020) 105824. https://doi.org/10.1016/j.compag.2020.105824 DOI: https://doi.org/10.1016/j.compag.2020.105824

V.K. Shrivastava, M.K. Pradhan, S. Minz, M.P. Thakur, Rice plant disease classification using transfer learning of deep convolution neural network, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 3 (2019)631–635.https://doi.org/10.5194/isprs-archives-XLII-3-W6-631-2019 DOI: https://doi.org/10.5194/isprs-archives-XLII-3-W6-631-2019

R. Rajmohan, M. Pajany, R. Rajesh, D.R. Raman, U. Prabu, Smart paddy crop disease identification and management using deep convolution neural network and SVM classifier, Int. J. Pure Appl. Math. 118 (2018) 255–264.

P.K. Sethy, C. Bhoi, N.K. Barpanda, S. Panda, S.K. Behera, A.K. Rath, Pest Detection and Recognition in Rice Crop Using SVM in Approach of Bag-Of-Words, in: Int. Conf. Softw. Syst. Process., 2017.

M.J. Hasan, S. Mahbub, M.S. Alom, M.A. Nasim, Rice disease identification and classification by integrating support vector machine with deep convolutional neural network, in: 2019 1st Int. Conf. Adv. Sci. Eng. Robot. Technol., IEEE, 2019: pp. 1–6. https://doi.org 10.1109/ICASERT.2019.8934568 DOI: https://doi.org/10.1109/ICASERT.2019.8934568

C.R. Rahman, P.S. Arko, M.E. Ali, M.A.I. Khan, S.H. Apon, F. Nowrin, A. Wasif, Identification and recognition of rice diseases and pests using convolutional neural networks, Biosyst. Eng. 194 (2020)112–120.https://doi.org/10.1016/j.biosystemseng.2020.03.020 DOI: https://doi.org/10.1016/j.biosystemseng.2020.03.020

S. Ghosal, K. Sarkar, Rice leaf diseases classification using CNN with transfer learning, in: 2020 IEEE Calcutta Conf., IEEE, 2020: pp. 230–236. https://doi.org/10.1109/CALCON49167.2020.9106423. DOI: https://doi.org/10.1109/CALCON49167.2020.9106423

M.E. Pothen, M.L. Pai, Detection of rice leaf diseases using image processing, in: 2020 Fourth Int. Conf. Comput. Methodol. Commun., IEEE, 2020: pp. 424–430.https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00080. DOI: https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00080

Paddy Doctor: Paddy Disease Classification | Kaggle, (n.d.).https://www.kaggle.com/competitions/paddy-disease-classification/data (accessed February 13, 2023). https://doi.org/10.17632/fwcj7stb8r.1.

“ImageNet Object Localization Challenge | Kaggle.” https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data (accessed Mar. 24, 2023).

N. Sharma, V. Jain, A. Mishra, An analysis of convolutional neural networks for image classification, Procedia Comput. Sci. 132 (2018) 377–384.https://doi.org/10.1016/j.procs.2018.05.198. DOI: https://doi.org/10.1016/j.procs.2018.05.198

H. Yu, S. Kim, SVM Tutorial-Classification, Regression and Ranking., Handb. Nat. Comput. 1 (2012) 479–506. DOI: https://doi.org/10.1007/978-3-540-92910-9_15

J. Sharma, O.-C. Granmo, M. Goodwin, J.T. Fidje, Deep convolutional neural networks for fire detection in images, in: Int. Conf. Eng. Appl. Neural Networks, Spriner,2017:pp.183–193.https://doi.org/10.1007/978-3-319-65172-9_16. DOI: https://doi.org/10.1007/978-3-319-65172-9_16

B. Leibe, J. Matas, N. Sebe, M. Welling, Springer International Publishing: Cham, (2016).

L. Metz, N. Maheswaranathan, R. Sun, C.D. Freeman, B. Poole, J. Sohl-Dickstein, Using a thousand optimization tasks to learn hyperparameter search strategies, ArXiv Prepr. ArXiv2002.11887. (2020). https://doi.org/10.48550/arXiv.2002.11887.

M. Chiaberge, A. Tartaglia, Machine Learning Algorithms for Service Robotics Applications in Precision Agriculture, (2018).

L. Baecker, R. Garcia-Dias, S. Vieira, C. Scarpazza, A. Mechelli, Machine learning for brain age prediction: Introduction to methods and clinical applications, EBioMedicine. 72 (2021) 103600.https://doi.org/10.1016/j.ebiom.2021.103600. [24] S. Ghosh, A. Dasgupta, A. Swetapadma, A study on support vector machine based linear and non-linear pattern classification, in: 2019 Int. Conf. Intell. Sustain. Syst., IEEE, 2019: pp. 24–28. https://doi.org/10.1109/ISS1.2019.8908018 DOI: https://doi.org/10.1016/j.ebiom.2021.103600

M.A. Khan, K. Abbas, M.M. Su’ud, A.A. Salameh, M.M. Alam, N. Aman, M. Mehreen, A. Jan, N.A.A.B.N. Hashim, R.C. Aziz, Application of Machine Learning Algorithms for SustainableBusiness Management Based on Macro-Economic Data: Supervised Learning Techniques Approach, Sustbiity.14(2022)9964.https://doi.org/10.3390/su14169964. DOI: https://doi.org/10.3390/su14169964

A. Rana, P. Vaidya, G. Gupta, A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm, Mater. Today Proc. 56 (2022) 2025–2030. https://doi.org/10.1016/j.matpr.2021.11.350. DOI: https://doi.org/10.1016/j.matpr.2021.11.350

Z. Mehmood, S. Asghar, Customizing SVM as a base learner with AdaBoost ensemble to learn from multiclass problems: A hybrid approach AdaBoost-MSVM, Knowledge-Based Syst. 217 (2021) 106845. https://doi.org/10.1016/j.knosys.2021.106845 DOI: https://doi.org/10.1016/j.knosys.2021.106845

J. Park, Y. Choi, J. Byun, J. Lee, S. Park, Efficient differentially private kernel support vector classifier for multiclass classification, Inf. Sci. (Ny). 619 (2023) 889–907. https://doi.org/10.1016/j.ins.2022.10.075. DOI: https://doi.org/10.1016/j.ins.2022.10.075

K.-B. Duan, S.S. Keerthi, Which is the best multiclass SVM method? An empirical study, in: Mult. Classif. Syst. 6th Int. Work. MCS 2005, Seaside, CA, USA, June 13-15, 2005. Proc. 6, Springer, 2005: pp. 278–285. https://doi.org/10.1007/11494683_28 DOI: https://doi.org/10.1007/11494683_28

Z.-L. Zhang, C.-Y. Zhang, X.-G. Luo, Q. Zhou, A multiple classifiers system with roulette-based feature subspace selection for one-vs-one scheme, Pattern Anal. Appl. (2022) 1–18. DOI: https://doi.org/10.1007/s10044-022-01089-w

S. Kang, Using binary classifiers for one-class classification, Expert Syst. Appl. 187 (2022) 115920.https://doi.org/10.1016/j.eswa.2021.115920. DOI: https://doi.org/10.1016/j.eswa.2021.115920




How to Cite

Alwan, douaa S., & Naji, M. (2023). Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling. Journal of Kufa for Mathematics and Computer, 10(1), 96–101. https://doi.org/10.31642/JoKMC/2018/100114

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.