Abstract
Sugarcane, belonging to the grass family Poaceae, is rich in sugar sucrose, thereby used for making white sugar, jaggery and other by-products like molasses and bagasse. However, a diseased sugarcane plant is of no use, so it needs to be detected as soon as possible. A novel deep learning framework approach is proposed in this paper to detect whether a sugarcane plant is diseased or not by analyzing its leaves, stem, color, etc. The study comprises three scenarios based on different feature extractors namely Inception v3, VGG-16 and VGG-19. These are the pertained models on which different classifiers are trained. The state-of-the-art algorithms (SVM, SGD, ANN, naive Bayes, KNN and logistic regression) are compared with deep learning algorithms like neural network and hybrid AdaBoost. Several statistical measures such as accuracy, precision, specificity, AUC and sensitivity are calculated using Orange software, and the scenario having the highest accuracy is chosen. The receiver operating characteristic curve is computed in order to assess accuracy. An AUC of 90.2% is obtained using VGG-16 as the feature extractor and SVM as the classifier.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
28 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42979-023-02168-3
References
Khan A, Yadav MS, Ahmad S. Image processing based disease detection for sugarcane leaves. Int J Adv Res Ideas Innov Technol. 2017;3(4):497–502.
Viswanathan R, Rao GP. Disease scenario and management of major sugarcane diseases in India. Sugar Tech. 2011;13(4):336–53.
Padhy JB, et al. Leaf disease detection using K-means clustering and fuzzy logic classifier. Int J Eng Stud Tech Approach. 2016;2:5.
Patil SB, Bodhe SK. Leaf disease severity measurement using image processing. Int J Eng Technol. 2011;3(5):297–301.
Badnakhe MR, Deshmukh PR. An application of K-means clustering and artificial intelligence in pattern recognition for crop diseases. In: International conference on advancements in information technology with workshop of ICBMG 2011 IPCSIT vol. 20; 2011.
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.
Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR. 2013;J15:211–7.
Kulkarni AH, Patil A. Applying image processing technique to detect plant diseases. Int J Modern Eng Res IJMER. 2012;2(5):3661–4.
Bashir Sabah, Sharma Navdeep. Remote area plant disease detection using image processing. IOSR J Electron Commun Eng IOSRJECE. 2012;2(6):31–4.
Ghazvinei PT, Darvishi HH, Mosavi A, Yusof KBW, Alizamir M, Shamshirband S. K-W Chau (2018) Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Eng Appl Comput Fluid Mech. 2018;12(1):738–49.
Nijhawan R, et al. Meta-classifier approach with ANN, SVM, rotation forest, and random forest for snow cover mapping. In: Proceedings of 2nd international conference on computer vision and image processing. Springer, Singapore; 2018.
Viswanathan R. Plant diseases: red rot of sugarcane. New Delhi: Anmol Publication Pvt. Ltd.; 2010. p. 22–40.
Ardabili SF, Najafi B, Alizamir M, Mosavi A, Shamshirb S, Rabczuk T. Using SVM-RSM and ELM-RSM approaches for optimizing the production process of methyl and ethyl esters. Energies. 2018;11:2889.
https://docs.biolab.si//3/data-mining-library/reference/evaluation.cd.html.
Chaudhary P, Chaudhari AK, Cheeran AN, Godara S. Color transform based approach for disease spot detection on plant leaf. Int J Comput Sci Telecommun. 2012;3(6):65–70.
Reddy BS, Deepa R, Shalini S, Divya PB. A novel machine learning based approach for detection and classification of sugarcane plant disease by using DWT. Int Res J Eng Technol 2017;4(12):843–846.
Dodge S, Karam L. Understanding how image quality affects deep neural networks. In: IEEE, pp 1–6; 2016.
Canziani A, Paszke A, Culurciello E. An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678; 2016.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Advances in Computational Intelligence, Paradigms and Applications” guest edited by Young Lee and S. Meenakshi Sundaram.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Srivastava, S., Kumar, P., Mohd, N. et al. A Novel Deep Learning Framework Approach for Sugarcane Disease Detection. SN COMPUT. SCI. 1, 87 (2020). https://doi.org/10.1007/s42979-020-0094-9
Published:
DOI: https://doi.org/10.1007/s42979-020-0094-9