Skip to main content

Advertisement

SpringerLink
Log in

Leaf Image-Based Plant Disease Identification Using Color and Texture Features

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on tenfold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.

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

Similar content being viewed by others

Availability of Data and Material

The data used for experiments is available at https://github.com/spMohanty/PlantVillage-Dataset.

Code Availability

The source code of the project will be made public after acceptance of the manuscript.

References

  1. Savary, S., Ficke, A., Aubertot, J. N., & Hollier, C. (2012). Crop losses due to diseases and their implications for global food production losses and food security. Springer.

    Book  Google Scholar 

  2. Pertot, I., Kuflik, T., Gordon, I., Freeman, S., & Elad, Y. (2012). Identificator: A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry. Computers and Electronics in Agriculture, 84, 144–154.

    Article  Google Scholar 

  3. Colwell, R. (1956). Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 26(5), 223–286.

    Article  Google Scholar 

  4. Mahlein, A.-K. (2016). Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease, 100(2), 241–251.

    Article  Google Scholar 

  5. Bock, C. H., & Nutter, F. W. (2011). Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis. Plant Sciences Reviews, 2012, 73.

    Google Scholar 

  6. Bock, C. H., Poole, G. H., Parker, P. E., & Gottwald, T. R. (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences, 29(2), 59–107.

    Article  Google Scholar 

  7. Shafi, R., Shuai, W., & Younus, M. U. (2020). 360-degree video streaming: A survey of the state of the art. Symmetry, 12(9), 1491.

    Article  Google Scholar 

  8. Shafi, R., Shuai, W., & Younus, M. U. (2020). MTC360: A multi-tiles configuration for viewport-dependent 360-degree video streaming. In 2020 IEEE 6th international conference on computer and communications (ICCC) (pp. 1868–1873). IEEE.

  9. Savary, S., Teng, P. S., Willocquet, L., & Nutter, F. W. (2006). Quantification and modeling of crop losses: A review of purposes. Annual Review of Phytopathology, 44, 89–112.

    Article  Google Scholar 

  10. Dhingra, G., Kumar, V., & Joshi, H. D. (2017). Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77, 1–50.

    Google Scholar 

  11. Dhingra, G., Kumar, V., & Joshi, H. D. (2018). Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77(15), 19951–20000.

    Article  Google Scholar 

  12. Hughes, D., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.

  13. Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96–107.

    Article  Google Scholar 

  14. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.

    Article  Google Scholar 

  15. Khandelwal, I., & Raman, S. (2019). Analysis of transfer and residual learning for detecting plant diseases using images of leaves. In Computational intelligence: Theories, applications and future directions (vol. II, pp. 295–306). Springer.

  16. Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.

    Article  Google Scholar 

  17. Zhang, S., Wang, H., Huang, W., & You, Z. (2018). Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik-International Journal for Light and Electron Optics, 157, 866–872.

    Article  Google Scholar 

  18. Goncharov, P., Ososkov, G., Nechaevskiy, A., & Uzhinskiy, A. (2018). Architecture and basic principles of the multifunctional platform for plant disease detection.

  19. Li, J., Jia, J., & Xu, D. (2018). Unsupervised representation learning of image-based plant disease with deep convolutional generative adversarial networks. In 2018 37th Chinese control conference (CCC). IEEE.

  20. Phadikar, S., Sil, J., & Das, A. K. (2013). Rice diseases classification using feature selection and rule generation techniques. Computers and Electronics in Agriculture, 90, 76–85.

    Article  Google Scholar 

  21. Baquero, D., Molina, J., Gil, R., Bojacá, C., Franco, H., & Gómez, F. (2014). An image retrieval system for tomato disease assessment. In 2014 XIX symposium on image, signal processing and artificial vision (STSIVA). IEEE.

  22. Patil, J. K., & Kumar, R. (2017). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Engineering in Agriculture, Environment and Food, 10(2), 69–78.

    Article  Google Scholar 

  23. Sandika, B., Avil, S., Sanat, S., & Srinivasu, P. (2016). Random forest based classification of diseases in grapes from images captured in uncontrolled environments. In 2016 IEEE 13th international conference on signal processing (ICSP). IEEE.

  24. Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M., & Borghese, A. N. (2014). Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity. Computers and Electronics in Agriculture, 104, 1–8.

    Article  Google Scholar 

  25. Sharif, M., Khan, M. A., Iqbal, Z., Azam, M. F., Lali, M. I. U., & Javed, M. Y. (2018). Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Computers and Electronics in Agriculture, 150, 220–234.

    Article  Google Scholar 

  26. Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41–49.

    Article  Google Scholar 

  27. Bai, X., Li, X., Fu, Z., Lv, X., & Zhang, L. (2017). A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Computers and Electronics in Agriculture, 136, 157–165.

    Article  Google Scholar 

  28. Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2018). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290.

    Article  Google Scholar 

  29. Yuan, Y., Fang, S., & Chen, L. (2018). Crop disease image classification based on transfer learning with DCNNs. Springer.

    Book  Google Scholar 

  30. Zeng, W., Li, M., Zhang, J., Chen, L., Fang, S., & Wang, J. (2018). High-order residual convolutional neural network for robust crop disease recognition. In Proceedings of the 2nd international conference on computer science and application engineering. ACM.

  31. Shafarenko, L., Petrou, M., & Kittler, J. (1997). Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing, 6(11), 1530–1544.

    Article  Google Scholar 

  32. Vala, H. J., & Baxi, A. (2013). A review on Otsu image segmentation algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2(2), 387–389.

    Google Scholar 

  33. Saleem, G., Akhtar, M., Ahmed, N., & Qureshi, W. S. (2019). Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture, 157, 270–280.

    Article  Google Scholar 

  34. Ahmed, N., Khan, U. G., & Asif, S. (2016). An automatic leaf based plant identification system. Science International, 28(1), 427–430.

    Google Scholar 

  35. Clausi, D. A. (2002). An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remote Sensing, 28(1), 45–62.

    Article  Google Scholar 

  36. Soh, L.-K., & Tsatsoulis, C. (1999). Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing, 37, 47.

    Article  Google Scholar 

  37. Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621.

    Article  Google Scholar 

  38. Santos, T. A., Maistro, C. E. B., Silva, C. B., Oliveira, M. S., França, M. C., & Castellano, G. (2015). MRI texture analysis reveals bulbar abnormalities in Friedreich ataxia. American Journal of Neuroradiology, 36, 2214–2218.

    Article  Google Scholar 

  39. Grus, J. (2015). Data science from scratch: First principles with python. O’Reilly Media, Inc.

    Google Scholar 

  40. Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491–502.

    Article  MathSciNet  Google Scholar 

  41. Islam, M., Dinh, A., Wahid, K., & Bhowmik, P. (2017). Detection of potato diseases using image segmentation and multiclass support vector machine. In 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE). IEEE.

  42. Hlaing, C. S., & Zaw, S. M. M. (2017). Plant diseases recognition for smart farming using model-based statistical features. In 2017 IEEE 6th global conference on consumer electronics (GCCE). IEEE.

  43. Hlaing, C. S., & Zaw, S. M. M. (2017). Model-based statistical features for mobile phone image of tomato plant disease classification. In 2017 18th international conference on parallel and distributed computing, applications and technologies (PDCAT). IEEE.

  44. Amara, J., Bouaziz, B., & Algergawy, A. (2017). A deep learning-based approach for banana leaf diseases classification. In BTW (workshops).

  45. Durmuş, H., Güneş, E. O., & Kırcı, M. (2017). Disease detection on the leaves of the tomato plants by using deep learning. In 2017 6th international conference on agro-geoinformatics. IEEE.

  46. Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep learning for tomato diseases: Classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299–315.

    Article  Google Scholar 

  47. 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 

  48. Wang, J., Chen, L., Zhang, J., Yuan, Y., Li, M., & Zeng, W. (2018). CNN transfer learning for automatic image-based classification of crop. In Image and graphics technologies and applications: 13th conference on image and graphics technologies and applications, IGTA 2018, Beijing, China, April 8–10, 2018, revised selected papers. Springer.

  49. Yadav, R., Rana, Y. K., & Nagpal, S. (2018). Plant leaf disease detection and classification using particle swarm optimization. Springer.

    Google Scholar 

  50. Kour, V. P., & Arora, S. (2019). Particle swarm optimization based support vector machine (P-SVM) for the segmentation and classification of plants. IEEE Access, 7, 29374–29385.

    Article  Google Scholar 

  51. Pardede, H. F., Suryawati, E., Sustika, R., & Zilvan, V. (2018). Unsupervised convolutional autoencoder-based feature learning for automatic detection of plant diseases. In 2018 international conference on computer, control, informatics and its applications (IC3INA). IEEE.

  52. Suryawati, E., Sustika, R., Yuwana, R. S., Subekti, A., & Pardede, H. F. (2018). Deep structured convolutional neural network for tomato diseases detection. In 2018 international conference on advanced computer science and information systems (ICACSIS). IEEE.

  53. Baranwal, S., Khandelwal, S., & Arora, A. (2019). Deep learning convolutional neural network for apple leaves disease detection. Available at SSRN 3351641.

Download references

Funding

There are no funding sources for this Project.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Engineering and Technology Lahore, Lahore, 54890, Pakistan

    Nisar Ahmad

  2. Department of Computer Science, University of Engineering and Technology Lahore, Lahore, 54890, Pakistan

    Hafiz Muhammad Shahzad Asif

  3. Department of Computer Science, COMSATS University Islamabad (Lahore Campus), Lahore, 54890, Pakistan

    Gulshan Saleem

  4. Ecole Mathématiques, Informatique, Télécommunications de Toulouse, Université de Toulouse, Toulouse, France

    Muhammad Usman Younus

  5. Department of Business Development and Technology, Aarhus University, Herning, Denmark

    Sadia Anwar

  6. Department of Electronic Engineering, The Islamia University of Bahawalpur, 63100, Bahawalpur, Pakistan

    Muhammad Rizwan Anjum

  7. Department of Electrical and Computer Engineering, COMSATS University Islamabad Sahiwal Campus, Sahiwal, 57000, Pakistan

    Muhammad Usman Younus

Authors
  1. Nisar Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hafiz Muhammad Shahzad Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gulshan Saleem
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Usman Younus
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sadia Anwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhammad Rizwan Anjum
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The contributions of the authors are listed below: NA: Conception, coding, experimentations and writeup. HMSA: Conception, supervision and review. GS: Coding, experimentations, writeup and review. MUY: experimentations, writeup and review. SA: Proof Read and reviewed the manuscript. MRA: Proof Read and reviewed the manuscript.

Corresponding author

Correspondence to Muhammad Usman Younus.

Ethics declarations

Conflict of interest

It is declared that there are no financial and other competing conflicts of interests.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmad, N., Asif, H.M.S., Saleem, G. et al. Leaf Image-Based Plant Disease Identification Using Color and Texture Features. Wireless Pers Commun 121, 1139–1168 (2021). https://doi.org/10.1007/s11277-021-09054-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09054-2

Keywords

Search

Navigation