Author(s):
Ajay Kumar Dewangan, Sanjay Kumar, Tej Bahadur Chandra
Email(s):
ajaydewangan1212@gmail.com , tejbahadur1990@gmail.com
DOI:
10.52711/0974-360X.2022.00423 
Address:
Ajay Kumar Dewangan1*, Sanjay Kumar2, Tej Bahadur Chandra3
1,2Department of Computer Science, Kalinga University, Naya, Raipur, Chhattisgarh, India.
3Department of Computer Applications, National Institute of Technology, Raipur, Chhattisgarh, India.
*Corresponding Author
Published In:
Volume - 15,
Issue - 6,
Year - 2022
ABSTRACT:
Automatic approaches for detecting wheat plant diseases at an early stage are critical for protecting the plants and improving productivity. In the traditional system, farmers use their naked eyes to identify the disease, which is time-consuming and requires domain knowledge. In addition, the domain experts in many remote areas are not available in time and are expensive. To address the above issues, this study proposed an automatic wheat plant disease classification using combined features and an optimized ensemble learning algorithm. The main objective of the proposed system is to detect and classify the normal vs leaf rust vs nitrogen-deficient in wheat plants. Further, we used 1459 wheat leaf images from a public dataset to evaluate the suggested method. From the experimental results (ACC=96.00% for normal vs nitrogen deficient, ACC=98.25% for normal vs leaf rust and ACC=97.39% for normal vs leaf rust vs nitrogen deficient), it is observed that the suggested ensemble method outperformed the other benchmark machine learning algorithms.
Cite this article:
Ajay Kumar Dewangan, Sanjay Kumar, Tej Bahadur Chandra. Leaf-Rust and Nitrogen Deficient Wheat Plant Disease Classification using Combined Features and Optimized Ensemble Learning. Research Journal of Pharmacy and Technology. 2022; 15(6):2531-8. doi: 10.52711/0974-360X.2022.00423
Cite(Electronic):
Ajay Kumar Dewangan, Sanjay Kumar, Tej Bahadur Chandra. Leaf-Rust and Nitrogen Deficient Wheat Plant Disease Classification using Combined Features and Optimized Ensemble Learning. Research Journal of Pharmacy and Technology. 2022; 15(6):2531-8. doi: 10.52711/0974-360X.2022.00423 Available on: https://rjptonline.org/AbstractView.aspx?PID=2022-15-6-27
REFERENCES:
1. World Food and Agriculture - Statistical Yearbook 2020. FAO; 2020. doi:10.4060/cb1329en
2. Patil SA, Khot DS, Otari OD, Malavkar UG. Automatic Detection and Classification of Plant Disease through Image Processing. In; 2013.
3. Varinderjit Kaur AO. A Survey of Image Processing Technique for Wheat Disease Detection. Int J Emerg Technol Eng Res. 2017;5(12):133-137.
4. Ghaiwat SN, Arora P. Detection and Classification of Plant Leaf Diseases Using Image processing Techniques: A Review. Int J Recent Adv Eng Technol ISSN (Online. 2014;(2):2347-2812.
5. Sanjay B. Dhaygude NPK, Dhaygude SB, Kumbhar NP. Agricultural plant leaf disease detection using image processing. Int J Adv Res Electr Electron Instrum Eng. 2013;2(1):599-602.
6. Figueroa M, Hammond-Kosack KE, Solomon PS. A review of wheat diseases-a field perspective. Mol Plant Pathol. 2018;19(6):1523-1536. doi:10.1111/mpp.12618
7. Ashourloo D, Aghighi H, Matkan AA, Mobasheri MR, Rad AM. An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement. IEEE J Sel Top Appl Earth Obs Remote Sens. 2016;9(9):4344-4351. doi:10.1109/JSTARS.2016.2575360
8. BOLTON MD, KOLMER JA, GARVIN DF. Wheat leaf rust caused by Puccinia triticina. Mol Plant Pathol. 2008;9(5):563-575. doi:10.1111/j.1364-3703.2008.00487.x
9. Huerta-Espino J, Singh RP, Germán S, et al. Global status of wheat leaf rust caused by Puccinia triticina. Euphytica. 2011;179(1):143-160. doi:10.1007/s10681-011-0361-x
10. Murray GM, Brennan JP. Estimating disease losses to the Australian barley industry. Australas Plant Pathol. 2010;39(1):85. doi:10.1071/AP09064
11. Liu L, Dong Y, Huang W, et al. A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery. IEEE Access. 2020;8:52181-52191. doi:10.1109/ACCESS.2020.2980310
12. Johannes A, Picon A, Alvarez-Gila A, et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric. 2017;138:200-209. doi:10.1016/J.COMPAG.2017.04.013
13. Xiaolong L, Zhanhong M, Bienvenido F, Feng Q, Haiguang W, Alvarez-Bermejo J. Development of automatic counting system for urediospores of wheat stripe rust based on image processing. Int J Agric Biol Eng. 2017;10(5):134-143. doi:10.25165/j.ijabe.20171005.3084
14. Urkude G, Pandey M. Contextual triple inference using a semantic reasoner rule to reduce the weight of semantically annotated data on fail–safe gateway for WSN. J Ambient Intell Humaniz Comput. Published online January 6, 2021. doi:10.1007/s12652-020-02836-9
15. Urkude G, Pandey M. AgriOn: a comprehensive ontology for Green IoT based agriculture. J Green Eng. 2020;10(9):7078-7101.
16. Lu J, Hu J, Zhao G, Mei F, Zhang C. An in-field automatic wheat disease diagnosis system. Comput Electron Agric. 2017;142:369-379. doi:10.1016/j.compag.2017.09.012
17. Altaf Hussain, Mohsin Ahmad, Imran Ahmad Mughal, Ali Haider. Automatic disease detection in wheat crop using convolution neural network. In: In The 4th International Conference on Next Generation Computing. 2018.; 2011. doi:10.13140/RG.2.2.14191.46244
18. Ajay Kumar SK. Automatic Detection and Classification of Wheat PlantDiseases Using Images Based Supervised Model. J Sci Comput. 2021;10(11):12-25. doi:16.10089.JSC.2021.V10I11.285311.2900
19. Chandra TB, Verma K, Jain D, Netam SS. Segmented Lung Boundary Correction in Chest Radiograph Using Context-Aware Adaptive Scan Algorithm. In: Proceedings of ICBEST 2018. Springer, Singapore; 2021:263-275. doi:10.1007/978-981-15-6329-4_23
20. Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Trans Syst Man Cybern. 1973;SMC-3(6):610-621. doi:10.1109/TSMC.1973.4309314
21. Gomez W, Pereira WCA, Infantosi AFC. Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound. IEEE Trans Med Imaging. 2012;31(10):1889-1899. doi:10.1109/TMI.2012.2206398
22. Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Vol 1. IEEE; 2005:886-893. doi:10.1109/CVPR.2005.177
23. Santosh KC, Antani S. Automated Chest X-Ray Screening: Can Lung Region Symmetry Help Detect Pulmonary Abnormalities? IEEE Trans Med Imaging. 2018;37(5):1168-1177. doi:10.1109/TMI.2017.2775636
24. Chandra TB, Verma K. Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm. In: Chaudhuri BB, and Nakagawa M, and Khanna P, and Kumar S, eds. Proceedings of Third International Conference on Computer Vision & Image Processing. Springer Singapore; 2020:21-33. doi:10.1007/978-981-32-9088-4_3
25. Chandra TB, Verma K, Singh BK, Jain D, Netam SS. Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme. Expert Syst Appl. 2020;158:113514. doi:10.1016/j.eswa.2020.113514
26. Chandra TB, Verma K. Analysis of quantum noise-reducing filters on chest X-ray images: A review. Measurement. 2020;153:107426. doi:10.1016/j.measurement.2019.107426
27. Chandra TB, Verma K, Jain D, Netam SS. Localization of the Suspected Abnormal Region in Chest Radiograph Images. In: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T). IEEE; 2020:204-209. doi:10.1109/ICPC2T48082.2020.9071445
28. Al-Tashi Q, Md Rais H, Abdulkadir SJ, Mirjalili S, Alhussian H. A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification. Published online 2020:273-286. doi:10.1007/978-981-32-9990-0_13
29. Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Adv Eng Softw. 2014;69:46-61. doi:10.1016/j.advengsoft.2013.12.007
30. Emary E, Zawbaa HM, Hassanien AE. Binary grey wolf optimization approaches for feature selection. Neurocomputing. 2016;172:371-381. doi:10.1016/j.neucom.2015.06.083
31. Rokach L. Ensemble-based classifiers. Artif Intell Rev. 2010;33(1-2):1-39. doi:10.1007/s10462-009-9124-7
32. Snoek J, Larochelle H, Adams RP. Practical Bayesian Optimization of Machine Learning Algorithms. (Weinberger FP and CJCB and LB and KQ, ed.). Curran Associates, Inc.; 2012.
33. Raj Kumar PA, Selvakumar S. Distributed denial of service attack detection using an ensemble of neural classifier. Comput Commun. 2011;34(11):1328-1341. doi:10.1016/j.comcom.2011.01.012
34. Garcıa Adeva JJ, Cervino Beresi U, Calvo RA. Accuracy and Diversity in Ensembles of Text Categorisers. CLEI Electron J. 2005;8(2). doi:10.19153/cleiej.8.2.1
35. Shalev-Shwartz S, Ben-David S. Understanding Machine Learning: From Theory to Algorithms. Cambridge university press; 2014.
36. Vapnik V. Statistical Learning Theory. 1998. Vol 3. Wiley, New York; 1998.
37. Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques.; 2012. doi:10.1016/B978-0-12-381479-1.00001-0
38. Chandra TB, Verma K, Singh BK, Jain D, Netam SS. Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert Syst Appl. 2021;165:113909. doi:10.1016/j.eswa.2020.113909