Vol.:(0123456789)

SN Computer Science (2020) 1:87 https://doi.org/10.1007/s42979-020-0094-9

SN Computer Science

ORIGINAL RESEARCH

A Novel Deep Learning Framework Approach for Sugarcane Disease Detection

Sakshi Srivastava1 · Prince Kumar1 · Noor Mohd1 · Anuj Singh2 · Fateh Singh Gill3

Published online: 14 March 2020 © Springer Nature Singapore Pte Ltd 2020

AbstractSugarcane, 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, preci-sion, 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.

Keywords Sugarcane · Deep learning · VGG-19 · Inception v3 · VGG-16

Introduction

From the grass family Poaceae, sugarcane is a crop rich in sugar sucrose. It is used for producing white sugar, jaggery and other by-products like molasses and bagasse. 75% of the global sugar is produced with the help of sugarcane only. The largest consumer and the second largest producer of sugar is India. India is also the second largest agriculture-based industry [1]. Consumption of sugarcane also prevents prostate and breast cancer as its juice is alkaline in nature. It is also beneficial for proper functioning of liver and kidney

and also controls blood pressure. However, the sugarcane plant has witnessed epidemics of diseases which result in degradation of the crop [2]. Diseased sugarcane highly affects the production.

It is important to monitor the health and disease for proper cultivation of a crop. Deep learning and image pro-cessing can be applied for detecting a diseased leaf, stem, fruit, color of affected area, size and shape of leaf, etc. [3]. Since sugarcane is a long duration crop (10–16 months), it is often attacked with diseases as mentioned in the next sec-tion [2]. Figure 1a, b shows a diseased and non-diseased sugarcane crop. Many deep learning algorithms are used in distinguishing between a diseased and a non-diseased sug-arcane plant.

Diseases of Sugarcane

Sugarcane plant can be affected by many types of diseases caused by some bacteria, fungi, viruses, protozoans or phytoplasma. Some of the sugarcane diseases are red rot, mosaic disease, ring spot and grassy shoot. The red root is also known as cancer of sugarcane and is caused by Colle-totrichum falcatum Went [4] (Fig. 2a).

This article is part of the topical collection “Advances in Computational Intelligence, Paradigms and Applications” guest edited by Young Lee and S. Meenakshi Sundaram.

* Sakshi Srivastava sakshigunja@gmail.com

1 Computer Science and Engineering Department, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

2 Technology Business Incubator, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

3 Department of Allied Sciences, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

SN Computer Science (2020) 1:8787 Page 2 of 7

SN Computer Science

Mosaic disease is widely observed in Co740 in Maha-rashtra, India. It is caused by sugarcane mosaic virus [2] (Fig. 2b).

Ring Spot Older leaves of sugarcane are generally affected by this disease. It is caused by fungus Leptosphaeria sac-chari (Fig. 2c).

Grassy Shoot It is caused by phytoplasma. The sugarcane leaves affected by grassy shoot cannot produce chlorophyll (Fig. 2d) (Table 1).

Literature Survey

The techniques for classification and detection of sugarcane disease have been proposed by several researchers as image processing was used for extracting the features from the plant and then detect whether it is diseased or not [1, 3].

Color transformation structure was used for performing texture analysis on the plant leaf, and then, the disease was detected using SVM classifier [5]. K-means clustering and artificial intelligence were used in addition to digital image processing in order to recognize patters for crop diseases [6]. Gabor filtering and segmentation were done on the leaf, and ANN was trained for classification [7]. Color analysis is done, and naive Bayes classifier was used for distinguishing between a diseased and non-diseased plant after extraction of required features and textures [8].

YCbCr, HSI and CIELAB color models were compared, and a part of CIELAB model was used on which median filter was applied in order to find out the diseased spot on the plant leaf [9]. Discrete wavelength transform algorithm was used to detect the disease in sugarcane, and the image classification was done using decision tree [9].

An elementary learning machine (ELM) was used to pre-dict growth of sugarcane in various parts of the country. Results showed that ELM model was better in comparison with conventional ANN algorithm [10].

Dataset Formation

Image acquisition and data collection play a vital role in proper functioning of any deep learning framework. In our case, the dataset was formed by collecting images of dis-eased and non-diseased sugarcane from Mawana Sugar Mill Pvt. Ltd. It was then divided into two sets—training set and test set. A total of 160 images were present in training set and 80 images in test set (Table 2).

Fig. 1 a A fresh sugarcane crop and b a diseased sugarcane crop

Fig. 2 a Sugarcane affected with red rot disease, b sugarcane plant leaf affected by mosaic disease, c sugarcane plant leaf affected by ring spot disease and d sugarcane plant leaf affected by grassy shoot disease

Table 1 Some other sugarcane diseases

Disease Caused by

Leaf scald Xanthomonas axonopodis pv. vasculorumMottled stripe Herbaspirillum ubrisubalbicansRatoon stunting disease Leifsonia xyli subsp. xyliRed stripe (top rot) Acidovorax avenae subsp.Black rot Ceratocystis adiposa Chalara sp. [anamo-

rph]Schizophyllum rot Schizophyllum commune

SN Computer Science (2020) 1:87 Page 3 of 7 87

SN Computer Science

Methodology

We have created three scenarios which use different types of feature extractors. These features are then scrutinized using seven classifiers namely support vector machine (SVM), KNN (K-nearest neighbor), neural network (NN), stochas-tic gradient descent (SGD), AdaBoost, logistic regression (LR) and naive Bayes. We have used Orange software for this purpose.

Orange is an open-source data mining toolkit used for visualization of data, machine learning and data analysis. The components or tools in Orange with the help of which data are visualized and the classifiers are applied are known as widgets. Programming in Orange is done visually in the workflow window. Pre-defined widgets are used by linking them together in the workflow. This workflow is considered as a canvas interface.

Firstly, we have loaded the dataset in the Import Image widget of Orange and then selected different feature extrac-tors from Image Embedding widget. Then, different classi-fiers are connected with the test and score widget in order to compute the accuracy as shown in Fig. 6.

Scenario 1

An Inception v3 [11], novel deep learning model is used as a feature extractor in this scenario. The features that are extracted are then fed to different classifiers. Inception v3 consists of two parts. The first part consists of feature extrac-tion with CNN, and the second includes classification using softmax and fully connected layers. Logistic regression gives the best accuracy for Inception v3 model. We obtain an AUC value of 0.917 and CA of 0.838 using logistic regression.

Scenario 2

A VGG-19 novel deep learning model is used as a feature extractor in this scenario. The features that are extracted are then fed to different classifiers [12]. VGG-19 is a 19-layer network created by “Visual Geometry Group” from

University of Oxford. VGG-19 obtains 9.0% error rate. The best accuracy for VGG-19 model is given by stochastic gra-dient descent (SGD). We obtain an AUC value of 0.863 and CA of 0.863 using SGD (Figs. 3, 4, 5).

Scenario 3 (Proposed Work)

Our proposed architecture is represented via the third sce-nario. It uses VGG-16 deep learning model as a feature extractor for the dataset. The extracted features are then fed to different classifiers. A VGG-16 model is a pre-trained deep learning model created by “Visual Geometry Group” from University of Oxford. It is a 16 weight layered network consisting of multiple 3 × 3 kernel-sized filters, trained on the ImageNet dataset. In a traditional VGG-16 model, the depth of the network is increased so that more complex fea-tures can be learned. The convolutional layers in VGG are followed by three fully connected layers.

A VGG-16 model is a pre-trained deep learning model created by “Visual Geometry Group” from University of Oxford. It is a 16-layer network consisting of multiple 3 × 3 kernel-sized filters, trained on the ImageNet dataset. In a traditional VGG-16 model, the depth of the network is increased so that more complex features can be learned. The convolutional layers in VGG are followed by three fully con-nected layers [13].

Result and Discussion

Out of the three scenarios, the proposed work is chosen by considering the highest classification accuracy (CA) value. If the CA value is same for two or models, then comparison is done on the basis of AUC (area under curve) value.

Since, VGG-16, VGG-19 and Inception v3 do not have same CA values, VGG-16 is chosen as it has maximum CA value of 0.844.

SVM can also be applied after using extreme learning machines (EMLs) which operates on feed forward networks having only one layer hidden [14].

This study uses different classifiers for different feature extractors namely Inception V3, VGG-16 and VGG-19. Their performance is depicted in Table 3. The proposed work gives the best accuracy using SVM.

Orange Scoring Methods

Scoring of a deep learning model is a necessary thing to do as it is very important to know how well a model is function-ing. The Orange Data Mining Library provides following scoring methods on the basis of which a classifier works. The test and score widget is used for this purpose. It scores

Table 2 Number of images present in the dataset used for disease detection

Dataset Category No. of images

Training set Diseased 80Non-diseased 80

Total 160Test set Diseased 40

Non-diseased 40Total 80Entire dataset 240

SN Computer Science (2020) 1:8787 Page 4 of 7

SN Computer Science

the classifiers on the basis of these classifiers by using scor-ing methods like AUC, CA, F1, precision and recall.

AUC (Area Under Curve) This value shows the perfor-mance of the proposed model. Closer the value of AUC is to 1, higher is the level of perfection of model. In our case, the AUC for the proposed approach is 0.902. The Orange Data Mining Library uses Orange.evaluation.AUC (results = None) for computing the value of AUC [15].

CA It is used for the calculation of the classification accu-racy which shows how efficiently does the deep learning

algorithm classify the images in a dataset, in our case, clas-sification between diseased and non-diseased sugarcane. The Orange Data Mining Library uses Orange.evaluation.CA (results = None) for computing the value of CA [15].

F1 It is used for computing the balanced F-score or F-measure. 1 is considered as the best value of F1, and 0 is considered as its worst value [15]. The Orange Data Min-ing Library uses Orange.evaluation.F1(results = None) for computing the value of F1. It is calculated using the follow-ing formula

Fig. 3 Feature Extraction using different feature extractors and classification using SVM, NN, KNN, SGD, AdaBoost, logistic regression and naive Bayes in Orange

SN Computer Science (2020) 1:87 Page 5 of 7 87

SN Computer Science

Precision It is calculated by dividing tp by sum of tp and fp where tp denotes the number of true positives and fp denotes the number of false positives. The Orange Data Mining Library uses Orange.evaluation.Precision (results = None) for computing the value of precision [15].

Recall It is calculated by dividing tp by sum of tp and fn where tp denotes the number of true positives and fp denotes the number of false negatives. The Orange Data Mining Library uses Orange.evaluation.Recall (results = None) for computing the value of recall [15].

Deep learning requires the measuring of performance of the model [16, 17]. The performance of the framework is evaluated by plotting the ROC curve (Fig. 6). For evaluat-ing the performance of a multi-class classification curve, receiver operating characteristics (ROC) curve is plotted. It is a probability curve. ROC is plotted with total positive rate (TPR) on the y-axis and FPR (false positive rate) on the x-axis. The x-axis of the ROC curve shows “1-specificity” value, and y-axis of that shows “sensitivity” [18, 19].

(1)F1 = 2 ∗ (precision ∗ recall)∕(precision + recall).

Conclusion

A novel deep learning approach was employed in our proposed work. Three different feature extractor models, namely VGG-16, VGG-19 and Inception V3, were used for extracting features. The output from the feature extractor models was then fed to seven different classifiers, namely naive Bayes, AdaBoost, neural network, stochastic gradi-ent descent, K-nearest neighbor, support vector machine and logistic regression; based on the CA and AUC values obtained, VGG-16 was chosen as the feature extractor model and SVM as the classifier. The higher CA value depends upon large dataset; hence, more accurate diseased detection can be done if more data are available. SVM was able to

Fig. 4 VGG 16 architecture

Fig. 5 VGG-16 model

SN Computer Science (2020) 1:8787 Page 6 of 7

SN Computer Science

classify sugarcane as diseased and non-diseased with the highest AUC, i.e., 90.2%. Further the same can be applied to various diseases in other plants or skin disease in human or animals.

References

1. 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.

2. Viswanathan R, Rao GP. Disease scenario and manage-ment of major sugarcane diseases in India. Sugar Tech. 2011;13(4):336–53.

3. Padhy JB, et al. Leaf disease detection using K-means cluster-ing and fuzzy logic classifier. Int J Eng Stud Tech Approach. 2016;2:5.

4. Patil SB, Bodhe SK. Leaf disease severity measurement using image processing. Int J Eng Technol. 2011;3(5):297–301.

5. 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.

6. 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.

7. Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV. Detec-tion of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR. 2013;J15:211–7.

Table 3 Accuracy assessment Model Classifier AUC CA F1 Precision Recall

Scenario 3 (proposed approach)VGG 16 SVM 0.902 0.844 0.843 0.850 0.844VGG 16 kNN 0.905 0.838 0.837 0.838 0.838VGG 16 Neural network 0.909 0.838 0.837 0.839 0.838VGG 16 Logistic regression 0.889 0.831 0.831 0.831 0.831VGG 16 AdaBoost 0.898 0.825 0.825 0.827 0.825VGG 16 SGD 0.806 0.806 0.806 0.807 0.806VGG 16 Naive Bayes 0.816 0.806 0.806 0.807 0.806Scenario 2VGG 19 SGD 0.863 0.863 0.862 0.865 0.863VGG 19 Logistic regression 0.923 0.850 0.850 0.851 0.850VGG 19 Neural network 0.909 0.844 0.844 0.844 0.844VGG 19 SVM 0.902 0.838 0.837 0.845 0.838VGG 19 AdaBoost 0.908 0.838 0.837 0.839 0.838VGG 19 kNN 0.908 0.831 0.830 0.838 0.831VGG 19 Naive Bayes 0.805 0.800 0.800 0.800 0.800Scenario 1Inception v3 Logistic regression 0.917 0.838 0.837 0.838 0.838Inception v3 SVM 0.897 0.819 0.818 0.827 0.819Inception v3 SGD 0.819 0.819 0.819 0.819 0.819Inception v3 Neural network 0.903 0.800 0.800 0.800 0.800Inception v3 kNN 0.923 0.794 0.793 0.796 0.794Inception v3 Naive Bayes 0.802 0.756 0.756 0.758 0.756Inception v3 AdaBoost 0.762 0.725 0.725 0.726 0.725

Fig. 6 ROC curve for different classifiers

SN Computer Science (2020) 1:87 Page 7 of 7 87

SN Computer Science

8. Kulkarni AH, Patil A. Applying image processing tech-nique to detect plant diseases. Int J Modern Eng Res IJMER. 2012;2(5):3661–4.

9. Bashir Sabah, Sharma Navdeep. Remote area plant disease detection using image processing. IOSR J Electron Commun Eng IOSRJECE. 2012;2(6):31–4.

10. Ghazvinei PT, Darvishi HH, Mosavi A, Yusof KBW, Alizamir M, Shamshirband S. K-W Chau (2018) Sugarcane growth pre-diction based on meteorological parameters using extreme learning machine and artificial neural network. Eng Appl Com-put Fluid Mech. 2018;12(1):738–49.

11. 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.

12. Viswanathan R. Plant diseases: red rot of sugarcane. New Delhi: Anmol Publication Pvt. Ltd.; 2010. p. 22–40.

13. https ://doi.org/10.1080/19942 060.2018.14824 76. 14. Ardabili SF, Najafi B, Alizamir M, Mosavi A, Shamshirb S, Rabc-

zuk T. Using SVM-RSM and ELM-RSM approaches for optimiz-ing the production process of methyl and ethyl esters. Energies. 2018;11:2889.

15. https ://docs.biola b.si//3/data-minin g-libra ry/refer ence/evalu ation .cd.html.

16. Chaudhary P, Chaudhari AK, Cheeran AN, Godara S. Color trans-form based approach for disease spot detection on plant leaf. Int J Comput Sci Telecommun. 2012;3(6):65–70.

17. 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.

18. Dodge S, Karam L. Understanding how image quality affects deep neural networks. In: IEEE, pp 1–6; 2016.

19. Canziani A, Paszke A, Culurciello E. An analysis of deep neural network models for practical applications. arXiv preprint arXiv :1605.07678 ; 2016.

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.