An Intelligent Framework for Rice Plant Disease Recognition and Classification using Deep Learning and Reptile Search Algorithm
An Intelligent Framework for Rice Plant Disease Recognition and Classification using Deep Learning and Reptile Search Algorithm |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-11 |
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| Year of Publication : 2023 | ||
| Author : D. Felicia Rose Anandhi, S. Sathiamoorthy |
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| DOI : 10.14445/22315381/IJETT-V71I11P218 | ||
How to Cite?
D. Felicia Rose Anandhi, S. Sathiamoorthy, "An Intelligent Framework for Rice Plant Disease Recognition and Classification using Deep Learning and Reptile Search Algorithm," International Journal of Engineering Trends and Technology, vol. 71, no. 11, pp. 171-180, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I11P218
Abstract
Rice is one of the majorly utilized primary crops globally, providing sustenance for the worldwide population’s crucial segment. However, if left untreated, rice plants are susceptible to various diseases that can cause substantial yield losses. Hence, precise and timely recognition of rice plant diseases is substantial for effectively managing the disease and ensuring optimal crop production. Therefore, this study concentrates on constructing an Automated Rice Plant Disease Detection utilizing a Reptile Search Algorithm with Deep Learning (ARPDD-RSADL) technique. The purpose of the ARPDD-RSADL technique lies in the accurate and proficient classification of rice plant ailments using a parameter-tuned DL model. To accomplish this, the ARPDD-RSADL technique performs image preprocessing in two stages: Unsharp Masking (UM) based filtering and contrast enhancement. In addition, the ARPDD-RSADL technique designs the Squeeze and Excitation ResNet (SE-ResNet) model for feature extraction purposes. The Stacked Sparse Auto-Encoder (SSAE) model is used with RSA based hyperparameter optimizer for plant disease detection. The design of RSA helps to adjust the parameters relevant to the SSAE model optimally, and it assists in attaining advanced accuracy in detection. An extensive experimentation analysis was accomplished to validate the effectual rice plant disease classification results of the ARPDD-RSADL method. The simulation values portrayed the ARPDD-RSADL method's excellence in diverse evaluation measures. Investigational outcomes illustrate the effectiveness of the proposed model in accurately classifying and recognizing several rice plant diseases.
Keywords
Rice plant disease, Computer vision, Agriculture, Deep learning, Reptile search algorithm.
References
[1] Santosh Kumar Upadhyay, and Avadhesh Kumar, “A Novel Approach for Rice Plant Diseases Classification with Deep Convolutional Neural Network,” International Journal of Information Technology, vol. 14, no. 1, pp. 185-199, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] K. Kishore Kumar, and E. Kannan, “Detection of Rice Plant Disease Using AdaBoostSVM Classifier,” Agronomy Journal, vol. 114, no. 4, pp. 2213-2229, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Wan-Jie Liang et al., “Rice Blast Disease Recognition Using a Deep Convolutional Neural Network,” Scientific Reports, vol. 9, pp. 1-10, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Norhalina Senan et al., “An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 7, pp. 116-122, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Ghulam Gilanie et al., “Ricenet: Convolutional Neural Networks-based Model to Classify Pakistani Grown Rice Seed Types,” Multimedia Systems, vol. 27, no. 5, pp. 867-875, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Shuai Feng et al., “A Deep Convolutional Neural Network-Based Wavelength Selection Method for Spectral Characteristics of Rice Blast Disease,” Computers and Electronics in Agriculture, vol. 199, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] N.V. Raja Reddy Goluguri, K. Suganya Devi, and P. Srinivasan, “Rice-net: An Efficient Artificial Fish Swarm Optimization Applied Deep Convolutional Neural Network Model for Identifying the Oryza Sativa Diseases,” Neural Computing and Applications, vol. 33, no. 11, pp. 5869-5884, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Narendra Pal Singh Rathore, and Lalji Prasad, “Automatic Rice Plant Disease Recognition and Identification Using Convolutional Neural Network,” Journal of Critical Reviews, vol. 7, no. 15, pp. 6076-6086, 2020.
[Google Scholar] [Publisher Link]
[9] Murat Koklu, Ilkay Cinar, and Yavuz Selim Taspinar, “Classification of Rice Varieties with Deep Learning Methods,” Computers and Electronics in Agriculture, vol. 187, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Manoj Agrawal, and Shweta Agrawal, “Rice Plant Diseases Detection and Classification Using Deep Learning Models: A Systematic Review,” Journal of Critical Reviews, vol. 7, no. 11, pp. 4376-4390, 2020.
[Google Scholar] [Publisher Link]
[11] Zhao Zhang et al., “A High-Quality Rice Leaf Disease Image Data Augmentation Method based on a Dual GAN,” IEEE Access, vol. 11, pp. 21176-21191, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Shweta Lamba, Anupam Baliyan, and Vinay Kukreja, “A Novel GCL Hybrid Classification Model for Paddy Diseases,” International Journal of Information Technology, vol. 15, pp. 1127-1136, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] L.B Zahro, Ika Hesti Agustin, and Zainur Rasyid Ridlo, Classification of Disease in Rice Plant Leaves Using the Method Convolutional Neural Networks, Proceedings of the 1 st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022), Springer Nature, vol. 177, pp. 195-216, 2023.
[Google Scholar] [Publisher Link]
[14] Yibin Wang, Haifeng Wang, and Zhaohua Peng, “Rice Diseases Detection and Classification Using Attention based Neural Network and Bayesian Optimization,” Expert Systems with Applications, vol. 178, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Minlan Jiang et al., “Rice Disease Identification Method based on Attention Mechanism and Deep Dense Network,” Electronics, vol. 12, no. 3, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] S. Senthil Pandi et al., “Rice Plant Disease Classification Using Dilated Convolutional Neural Network with Global Average Pooling,” Ecological Modelling, vol. 474, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Shankarnarayanan Nalini et al., “Paddy Leaf Disease Detection Using an Optimized Deep Neural Network,” Computers, Materials and Continua, vol. 68, no. 1, pp. 1117-1128, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Muhammad Hanif Tunio et al., “Identification and Classification of Rice Plant Disease Using Hybrid Transfer Learning,” In 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 525-529, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Xiaoqi Wang et al., “ECA-ConvNeXt: A Rice Leaf Disease Identification Model Based on ConvNeXt,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6234-6242, 2023.
[Google Scholar] [Publisher Link]
[20] S. Ramesh, and D. Vydeki “Recognition and Classification of Paddy Leaf Diseases Using Optimized Deep Neural Network with Jaya Algorithm,” Information Processing in Agriculture, vol. 7, no. 2, pp.249-260, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Vipul Narayan et al., “Enhance-Net: An Approach to Boost the Performance of Deep Learning Model Based on Real-Time Medical Images,” Journal of Sensors, vol. 2023, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Tie Chen et al., “A Non-Intrusive Load Monitoring Method based on Feature Fusion and SE-ResNet,” Electronics, vol. 12, no. 8, pp. 1- 14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Syed Furqan Qadri et al., “SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation,” Mathematics, vol. 10, no. 5, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Jithendra Thandra, and Shaik Sharief Basha, “A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm,” Diagnostics, vol. 13, no. 9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Harshadkumar B. Prajapati, Jitesh Shah, and Vipul K. Dabhi, “Detection and Classification of Rice Plant Diseases,” Intelligent Decision Technologies, vol. 11, no. 3, pp. 357-373, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[26] T. Nagarathinam, and K. Rameshkumar, “A Survey on Cluster Analysis Techniques for Plant Disease Diagnosis,” SSRG International Journal of Computer Science and Engineering, vol. 3, no. 6, pp. 11-17, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[27] R.P. Narmadha, N. Sengottaiyan, and R.J. Kavitha, “Deep Transfer Learning based Rice Plant Disease Detection Model,” Intelligent Automation and Soft Computing, vol. 31, no. 2, pp. 1257-1271, 2022.
[CrossRef] [Google Scholar] [Publisher Link]