ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access

Rice Plant Disease Identification Decision Support Model using Machine Learning


Affiliations
1 Department of Computer Science and Engineering, Sage University, India
 

   Subscribe/Renew Journal


In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted.

Keywords

Plant Disease Detection, Machine Learning, Image Processing, Food Security, Early Disease Detection
Subscription Login to verify subscription
User
Notifications
Font Size

  • FAO in India, “FAO in India”, Available at http://www.fao.org/india/fao-in-india/india-at-a-glance/en/, Available at 2021.

  • K. Golhani, S.K. Balasundram, G. Vadamalai and B. Pradhan, “A Review of Neural Networks in Plant Disease Detection using Hyperspectral Data”, Information Processing in Agriculture, Vol. 5, pp. 354-371, 2018 [3] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification”, Computational Intelligence and Neuroscience, Vol. 2016, pp. 1-11, 2016.

  • U. Shruthi, V. Nagaveni and B.K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection”, Proceedings of International Conference on Advanced Computing and Communication Systems, pp. 1-6, 2019.

  • Q.H. Cap, H. Tani, H. Uga, S. Kagiwada and H. Iyatomi, “Super-Resolution for Practical Automated Plant Disease Diagnosis System”, Proceedings of International Conference on Advanced Computing and Communication Systems, pp. 441-449, 2019.

  • E. Fujita and Y. Kawasaki, “Basic Investigation on a Robust and Practical Plant Diagnostic System”, Proceedings of International Conference on Machine Learning and Applications, pp. 330-339, 2016.

  • P. Sharma, Y.P.S. Berwal and W. Ghai, “Performance Analysis of Deep Learning CNN Models for Disease Detection in Plants using Image Segmentation”, Information Processing in Agriculture, Vol. 6, pp. 2214-3173, 2019.

  • A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg and D.P. Hughes, “Deep Learning for Image-Based Cassava Disease Detection”, Wireless Communications and Mobile Computing, Vol. 2017, pp. 1-8, 2017.

  • K.P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis”, Computers and Electronics in Agriculture, Vol. 145, pp. 311-318, 2018

  • E.C. Too, L. Yujian, S. Njuki and L. Yingchun, “A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification”, Computers and Electronics in Agriculture, Vol. 145, pp. 455-463, 2018.

  • G. Wang, Y. Sun and J. Wang, “Automatic Image-Based Plant Disease Severity Estimation using Deep Learning”, Computational Intelligence and Neuroscience, Vol. 2017, pp. 1-8, 2017.

  • D.O. Shamkuwar, G. Thakre, A.R. More, K.S. Gajakosh and M.O. Yewale, “An Expert System for Plant Disease Diagnosis by using Neural Network”, International Research Journal of Engineering and Technology, Vol. 5, No. 4, pp. 369-372, 2018.

  • S. Ramesh, R. Hebbar, M. Niveditha, R. Pooja, N.P. Bhat, N. Shashank and P.V. Vinod, “Plant Disease Detection using Machine Learning”, Proceedings of International Conference on Computer and Communications, pp. 1-14, 2018.

  • S.D. Khirade and A.B. Patil, “Plant Disease Detection using Image Processing”, Proceedings of International Conference on Computer and Communications, pp. 555-568, 2015.

  • V. Singh and A.K. Misra, “Detection of Plant Leaf Diseases using Image Segmentation and Soft Computing Techniques”, Information Processing in Agriculture, Vol. 4, pp. 41-49, 2017.

  • M. Islam, A. Dinh, K. Wahid and P. Bhowmik, “Detection of Potato Diseases using Image Segmentation and Multiclass Support Vector Machine”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-12, 2017.

  • D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat and N. Batra, “PlantDoc: A Dataset for Visual Plant Disease Detection”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-12, 2020.

  • S.S. Kumar and B.K. Raghavendra, “Diseases Detection of Various Plant Leaf Using Image Processing Techniques: A Review”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-13, 2019.

  • S.A. Nandhini, R. Hemalatha, S. Radha and K. Indumathi, “Web Enabled Plant Disease Detection System for Agricultural Applications using WMSN”, Wireless Personal Communications, Vol. 76, pp. 725-740, 2018.

  • H. Pourazar, F. Samadzadegan and F.D. Javan, “Aerial Multispectral Imagery for Plant Disease Detection: Radiometric Calibration Necessity Assessment”, European Journal of Remote Sensing, Vol. 52, No. 3, pp. 17-31, 2019.

  • J.P. Shah, H.B. Prajapati and V. K. Dabhi, “A Survey on Detection and Classification of Rice Plant Diseases”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-8, 2016.

  • P.S. Garud and R. Devi, “Detection of Diseases on Plant Leaf with the Help of Image Processing”, International Journal of Environmental Science and Technology, Vol. 4, No. 8, pp. 1-13, 2017.

  • M. Ray, A. Ray, S. Dash, A. Mishra, K.G. Achary, S. Nayak and S. Singh, “Fungal Disease Detection in Traditional Assays, Novel Diagnostic Techniques and Biosensors”, Biosensors and Bioelectronics, Vol. 87, pp. 708-723, 2017.

  • G. Dhingra, V. Kumar and H.D. Joshi, “Study of Digital Image Processing Techniques for Leaf Disease Detection and Classification”, Multimedia Tools and Applications, Vol. 78, pp. 1-14, 2017.

  • X. Nie, L. Wang, H. Ding and M. Xu, “Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention”, Vol. 7, IEEE Access, pp. 170003-170011, 2019

  • G. Owomugisha, E. Nuwamanya, J.A. Quinn, M. Biehl and E. Mwebaze, “Early Detection of Plant Diseases using Spectral Data”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-13, 2020.

  • S. Iniyan, R. Jebakumar, P. Mangalraj, M. Mohit and A. Nanda, “Plant Disease Identification and Detection using Support Vector Machines and Artificial Neural Networks”, Proceedings of International Conference on Advances in Intelligent Systems and Computing, pp. 15-27, 2020

  • K.P. Panigrahi, H. Das, A.K. Sahoo and S.C. Moharana, “Maize Leaf Disease Detection and Classification using Machine Learning Algorithms”, Proceedings of International Conference on Advances in Intelligent Systems and Computing, pp. 659-669, 2020.

  • Plant Village, Available at https://www.kaggle.com/abdallahalidev/plantvillage-dataset/version/1, Accessed at 2021.

  • Rice Leaf Disease, Available at https://www.kaggle.com/vbookshelf/rice-leaf-diseases, Accessed at 2021.

  • Canny Edge Detection, Available at https://www.cse.iitd.ac.in/~pkalra/col783-2017/canny.pdf, Accessed at 2009.

  • M. Nosrati, R. Karimi and M. Hariri, “Detecting Circular Shapes from Areal Images using Median Filter and CHT”, Global Journal of Computer Science and Technology, Vol 2, No. 1, pp. 49-54, 2012.

  • Z. Guo, L. Zhang and D. Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification”, IEEE Transactions on Image Processing, Vol. 19, No. 6, pp. 1657-1663, 2010

  • Y.G. Jiang, J. Yang, C.W. Ngo and A.G. Hauptmann, “Representations of Key Point-Based Semantic Concept Detection: A Comprehensive Study”, IEEE Transactions on Multimedia, Vol. 12, No. 1, pp. 42-53, 2008. Plants:


Abstract Views: 222

PDF Views: 89




  • Rice Plant Disease Identification Decision Support Model using Machine Learning

Abstract Views: 222  |  PDF Views: 89

Authors

Gaurav Shrivastava
Department of Computer Science and Engineering, Sage University, India
Harish Patidar
Department of Computer Science and Engineering, Sage University, India

Abstract


In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted.

Keywords


Plant Disease Detection, Machine Learning, Image Processing, Food Security, Early Disease Detection

References