Skip to main content

Advertisement

SpringerLink
Log in

Efficient feature selection using BoWs and SURF method for leaf disease identification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

When plant diseases appear, they have an adverse effect on agricultural output. Food insecurity would worsen if plant diseases were not identified accurately in time. Without early identification of plant diseases, agricultural production management and decision-making would not be feasible. Now a days deep learning model is widely used for image classification to get more accurate result. But machine learning based classifier also produces good result if good feature selection technique is used. In this paper, we have used hybrid machine learning techniques for classifying leaf diseases present in species such as tomato, potato and pepper bell. We have used bag-of- feature for visually representing diseased leaf features. SURF technique is used to extract strongest number of features and for classification task SVM is used. The proposed method gives good result in terms of precision, accuracy, recall, F1-score, FPR, FNR, and MCC on all the three employed datasets. The classification accuracy obtained in our proposed model on dataset1, dataset2 and dataset3 are 97%, 97% and 93% respectively. We have also calculated percentage feature reduction for all three types of species which are approximately 45.21%, 40.65% and 34.73% for tomato, potato and pepper bell respectively. We have compared disease classification accuracy of several previous work done by various authors on tomato leaf dataset using various machine learning and deep learning technique with our proposed work and find that our method is performing better.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available in the GitHub repository, https://github.com/MonuBhagat11/Leafdisease_data.

Abbreviations

SURF:

Speeded Up Robust Features

TTB:

Tomato Late Blight

TSLS:

Tomato Septoria leaf Spot

TTS:

Tomato Target Spot

TMV:

Tomato Mosaic virus

PBBS:

Pepper Bell Bacterial Spot

PEB:

Potato Early Blight

PH:

Potato Healthy

FPR:

False Positive Rate

DoGs:

Difference of Gaussians

PVD:

PlantVillge Dataset

GAN:

Generative Adversarial Network

TSWV:

Tomato spotted wilt virus

CNN:

Convolutional Neural network

BoWs:

Bag-of-words

TLM:

Tomato Leaf Mold

TTSSM:

Tomato Two Spotted Spider Mite

TYLCV:

Tomato Yellow leaf Curl Virus

TH:

Tomato Healthy

PBH:

Pepper Bell Healthy

PLB:

Potato Late Blight

MCC:

Mathews Correlation Co-efficient

FNR:

False Negative Rate

SVM:

Support Vector Machine

RFD:

Real field Dataset

SIFT:

Scale Invariant Feature Transform

PCA:

Percentage Classification Accuracy

SRCNN:

Super-Resolution Convolutional Neural Network

References

  1. Alahi MEEE, Pereira-Ishak N, Mukhopadhyay SC, Burkitt L (2018) An internet-of-things enabled smart sensing system for nitrate monitoring. IEEE Internet Things J 5(6):4409–4417

    Article  Google Scholar 

  2. Bay H, Tuytelaars T, Van Gool, L. (2006) SURF: Speeded up robust features. Computer Vision-ECCV 2006. 3951. 404–417. https://doi.org/10.1007/11744023_32

  3. Bhagat M, Kumar D (2022) A comprehensive survey on leaf disease identification & classification. Multimed Tools Appl 81:33897–33925. https://doi.org/10.1007/s11042-022-12984-z

    Article  Google Scholar 

  4. Bhagat M, Kumar D, Kumar D (2019) Role of internet of things (IoT) in smart farming: A brief survey. 2019 Devices for Integrated Circuit (DevIC), pp 141–145. https://doi.org/10.1109/DEVIC.2019.8783800

  5. Bhagat M, Kumar D, Haque I, Munda HS, Bhagat R (2020) Plant leaf disease classification using grid search based SVM. 2nd International Conference on Data, Engineering and Applications (IDEA), pp 1–6, https://doi.org/10.1109/IDEA49133.2020.9170725.

  6. Bhagat M, Kumar D, Mahmood R, Pati B, Kumar M (2020) Bell pepper leaf disease classification using CNN," 2nd International Conference on Data, Engineering and Applications (IDEA), pp 1–5, https://doi.org/10.1109/IDEA49133.2020.9170728.

  7. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Rev Plant Sci 29(2):59–107

    Article  Google Scholar 

  8. Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput Sci 54:764–771

    Article  Google Scholar 

  9. Durmus H, Gunes EO, Kirci M (2017) Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th Int Conf agro-Geoinformatics, Agro-Geoinformatics 2017. https://doi.org/10.1109/Agro-Geoinformatics.2017.8047016.

  10. Dutot M, Nelson LM, Tyson RC (2013) Predicting the spread of postharvest disease in stored fruit, with application to apples. Postharvest Biol Technol 85:45–56

    Article  Google Scholar 

  11. Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B (2017) Vision-based pest detection based on SVM classification method. Comput Electron Agric 137:52–58

    Article  Google Scholar 

  12. Elhassouny A, Smarandache F (2019) Smart mobile application to recognize tomato leaf diseases using convolutional neural networks. In: proc 2019 Int Conf Comput Sci renew energies, ICCSRE 2019 2019:1–4. https://doi.org/10.1109/ICCSRE.2019.8807737.

  13. Fina F, Birch P, Young R, Obu J, Faithpraise B, Chatwin C (2013) Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters. Int J Adv Biotechnol Res 4(2):189–199

    Google Scholar 

  14. Fuentes A, Yoon S, Kim S, Park D (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):2022

    Article  Google Scholar 

  15. Fuentes AF, Yoon S, Lee J, Park DS (2018) High-performance deep neural network-based tomato plant diseases and pests’ diagnosis system with refinement filter bank. Front Plant Sci 9:1–15. https://doi.org/10.3389/fpls.2018.01162

    Article  Google Scholar 

  16. Gaikwad SS, Rumma SS, Hangarge M (2022) Fungi affected fruit leaf disease classification using deep CNN architecture. Int J Inf Technol 14:3815–3824. https://doi.org/10.1007/s41870-022-00860-w

    Article  Google Scholar 

  17. Gupta A (2019) Current research opportunities for image processing and computer vision. Comput Therm Sci 20:387–410

    Google Scholar 

  18. Hlaing CS, Maung Zaw SM (2018) Tomato plant diseases classification using statistical texture feature and color feature. In: Proc - 17th IEEE/ACIS Int Conf Comput Inf Sci ICIS 2018 2018:439–44. https://doi.org/10.1109/ICIS.2018.8466483

  19. Hlaing CS, Zaw SMM (2017) Model-based statistical features for mobile phone image of tomato plant disease classification. In: Parallel Distrib Comput Appl Technol PDCAT Proc 2018; 2017-Decem: 223–9. https://doi.org/10.1109/PDCAT.2017.00044

  20. Jiang P, Chen Y, Liu B, He D, Liang C (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7:59069–59080

    Article  Google Scholar 

  21. Johannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Navajas AD et al (2017) Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric 138:200–209. https://doi.org/10.1016/j.compag.2017.04.013

    Article  Google Scholar 

  22. Koenderink J (1984) The structure of images. Biol Cybern 50:363–370

    Article  MATH  MathSciNet  Google Scholar 

  23. Li L, Zhang S, Wang B (2021) Plant disease detection and classification by deep learning—a review. IEEE Access 9:56683–56698

    Article  Google Scholar 

  24. Lowe D (2004) Distinctive image features from scale-invariant keypoints, cascade filtering approach. IJCV 60:91–110

    Article  Google Scholar 

  25. Mahlein A-K, Rumpf T, Welke P, Dehne HW, Plümer L, Steiner U, Oerke EC (2013) Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ 128:21–30

    Article  Google Scholar 

  26. Mikolajczyk K, Schmid C (2001) Indexing based on scale invariant interest points. In: ICCV. Volume 1, 525–531

  27. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. PAMI 27:1615–1630

    Article  Google Scholar 

  28. Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput Sci 133:1040–1047. https://doi.org/10.1016/j.procs.2018.07.070

    Article  Google Scholar 

  29. Tetila EC, Machado BB, de Souza Belete NA, Guimarães DA, Pistori H (2017) Identification of soybean foliar diseases using unmanned aerial vehicle images. IEEE Geosci Remote Sens Lett 14(12):2190–2194

    Article  Google Scholar 

  30. Trivedi M, Gupta A (2021) Automatic monitoring of the growth of plants using deep learning-based leaf segmentation. Int J Appl Sci Eng 18(2):1–9

    Google Scholar 

  31. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR (1), 511–518

  32. Wang D, Vinson R, Holmes M, Seibel G, Bechar A, Nof S, Tao Y (2019) Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). Sci Rep 9:4377

    Article  Google Scholar 

  33. Yamamoto K, Togami T, Yamaguchi N (2017) Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors 17(11):2557

    Article  Google Scholar 

  34. Yuan L, Huang Y, Loraamm RW, Nie C, Wang J, Zhang J (2014) Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Field Crop Res 156(2):199–207

    Article  Google Scholar 

  35. Zhang Y, Jin R, Zhou Z-H (2010) Understanding bag-of-words model: a statistical framework. Int J Mach Learn Cybern 1(1):43–52

    Article  Google Scholar 

  36. Zhang S, Huang W, Zhang C (2019) Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn Syst Res 53:31–41. https://doi.org/10.1016/j.cogsys.2018.04.006

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to the reviewers who provided valuable and insightful feedback.

Funding

This study received no specific financing from governmental, private, or non-profit funding bodies.

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand, India

    Monu Bhagat & Dilip Kumar

Authors
  1. Monu Bhagat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dilip Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monu Bhagat.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s note

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

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhagat, M., Kumar, D. Efficient feature selection using BoWs and SURF method for leaf disease identification. Multimed Tools Appl 82, 28187–28211 (2023). https://doi.org/10.1007/s11042-023-14625-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14625-5

Keywords

Search

Navigation