Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images

Abstract

Currently, researchers are developing numerous plant diseases recognition model using image processing and soft computing. The models are mainly based on the extraction of discolored features and applying in various soft computing approaches to automate plant diseases recognition process. The extracted features are statistical, frequency, spatial-frequency or hybrid features of captured images accessed in device-dependent or device-independent color spaces. The performance of diseases recognition system is significantly dependent upon the selection of color spaces and extracted features. This paper presents a comprehensive review of the impact of color spaces and feature sets on machine learning and rule base automated plant diseases classifier. The review performed with six categories of rice plant images with two machine learning and two rule base classifiers. Initially, a thorough literature review performed on the previous investigation based on color spaces and used feature sets for designing diseases recognition model. Then common conditions created to extract feature sets in different color spaces, and applied machine learning and rule base classifier to analyze the impact of color spaces with feature sets. The review presents a detailed discussion on the correlation between color spaces, feature sets, and performance of diseases recognition system. The review results reveal the most relevant features on specific color space for machine learning and rule base classifier. It also deduces that the performance of plant diseases classifier highly dependent upon used color space and extracted features.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

References

  1. 1.

    Kumar YS, Suhas G (2016) Identification and classification of fruit diseases. In: International conference on recent trends in image processing and pattern recognition. Springer, Singapore, pp 382–390

  2. 2.

    Muthukannan K, Latha P (2018) A GA_FFNN algorithm applied for classification in diseased plant leaf system. Multimed Tools Appl 77(18):24387–24403

    Article  Google Scholar 

  3. 3.

    Shrivastava S, Singh SK, Hooda DS (2015) Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation. Multimed Tools Appl 74(24):11467–11484

    Article  Google Scholar 

  4. 4.

    Shrivastava S, Singh SK, Hooda DS (2017) Soybean plant foliar disease detection using image retrieval approaches. Multimed Tools Appl 76(24):26647–26674

    Article  Google Scholar 

  5. 5.

    Pantazi XE, Moshou D, Tamouridou AA (2019) Automated leaf disease detection in different crop species through image features analysis and one class classifiers. Comput Electron Agric 156:96–104

    Article  Google Scholar 

  6. 6.

    Ali H, Lali MI, Nawaz MZ, Sharif M, Saleem BA (2017) Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92–104

    Article  Google Scholar 

  7. 7.

    Hassanien AE, Gaber T, Mokhtar U, Hefny H (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86–96

    Article  Google Scholar 

  8. 8.

    Mondal D, Kole DK, Roy K (2017) Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves. Comput Electron Agric 142:485–493

    Article  Google Scholar 

  9. 9.

    Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY (2018) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220–234

    Article  Google Scholar 

  10. 10.

    Mwebaze E, Owomugisha G (2016) Machine learning for plant disease incidence and severity measurements from leaf images. In: 15th IEEE international conference on machine learning and applications (ICMLA), 2016, pp 158–163

  11. 11.

    Islam T, Sah M, Baral S, RoyChoudhury R (2018) A faster technique on rice disease detection using image processing of affected area in agro-field. In: 2018 Second international conference on inventive communication and computational technologies (ICICCT). IEEE, pp 62–66

  12. 12.

    Agustina E, Pratomo I, Wibawa AD, Rahayu S (2017) Expert system for diagnosis pests and diseases of the rice plant using forward chaining and certainty factor method. In: Intelligent technology and its applications (ISITIA), international seminar. IEEE, pp 266–270

  13. 13.

    Asfarian A, Herdiyeni Y, Rauf A, Mutaqin KH (2013) Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. In: 2013 International conference on computer, control, informatics and its applications (IC3INA). IEEE, pp 77–81

  14. 14.

    Ghyar BS, Birajdar GK (2017) Computer vision based approach to detect rice leaf diseases using texture and color descriptors. In: International conference on inventive computing and informatics (ICICI). IEEE, pp 1074–1078

  15. 15.

    Joshi AA, Jadhav BD (2016) Monitoring and controlling rice diseases using image processing techniques. In: International conference on computing, analytics and security trends (CAST). IEEE, pp 471–476

  16. 16.

    Kalita H, Sarma SK, Choudhury RD (2016) Expert system for diagnosis of diseases of rice plants: prototype design and implementation. In: International conference on automatic control and dynamic optimization techniques ICACDOT). IEEE, pp 723–730

  17. 17.

    Liu L, Zhou G (2009) Extraction of the rice leaf disease image based on BP neural network. In: International conference on computational intelligence and software engineering, 2009. CiSE 2009. IEEE, pp 1–3

  18. 18.

    Orillo JW, Cruz JD, Agapito L, Satimbre PJ, Valenzuela I (2014) Identification of diseases in rice plant (Oryza sativa) using back propagation artificial neural network. In: 2014 International conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM). IEEE, pp 1–6

  19. 19.

    Phadikar S, Goswami J (2016) Vegetation indices based segmentation for automatic classification of brown spot and blast diseases of rice. In: 2016 3rd International conference on recent advances in information technology (RAIT). IEEE, pp 284–289

  20. 20.

    Pinki FT, Khatun N, Islam SM (2017) Content based paddy leaf disease recognition and remedy prediction using support vector machine. In: 2017 20th International conference on computer and information technology (ICCIT). IEEE, pp 1–5

  21. 21.

    Iqbal Z, Khan MA, Sharif M, Shah JH, ur Rehman MH, Javed K (2018) An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput Electron Agric 153:12–32

    Article  Google Scholar 

  22. 22.

    Patrício DI, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput Electron Agric 153:69–81

    Article  Google Scholar 

  23. 23.

    Shah JP, Prajapati HB, Dabhi VK (2016) A survey on detection and classification of rice plant diseases. In: IEEE international conference on current trends in advanced computing (ICCTAC). IEEE, pp 1–8

  24. 24.

    Delgado-Vera C, Mite-Baidal K, Gomez-Chabla R, Solís-Avilés E, Merchán-Benavides S, Rodríguez A (2018) Use of technologies of image recognition in agriculture: systematic review of literature. In: International conference on technologies and innovation. Springer, Cham, pp 15–29

  25. 25.

    Dhingra G, Kumar V, Joshi HD (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl 77(15):19951–20000

    Article  Google Scholar 

  26. 26.

    Giorgianni EJ, Madden TE, Spaulding KE (2003) Color management for digital imaging systems. Digital color imaging handbook, CRC Press, Boca Raton, pp 239–268

  27. 27.

    Balasubramanian R (2003) Device characterization. Digital color imaging handbook, pp 269–379

  28. 28.

    Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River

    Google Scholar 

  29. 29.

    Gonzalez RC, Eddins SL, Woods RE (2004) Digital image processing using MATLAB. Prentice Hall, Upper Saddle River

    Google Scholar 

  30. 30.

    Gao R, Liu G, Si Y (2010) A recognition method of apples based on texture features. In: World automation congress (WAC). IEEE, pp 225–229

  31. 31.

    Spiegel MR, Schiller JJ, Srinivasan RA, LeVan M (2009) Probability and statistics, vol 2. Mcgraw-hill, New York

    Google Scholar 

  32. 32.

    Kothari CR (2004) Research methodology: methods and techniques. New Age International, New Delhi

    Google Scholar 

  33. 33.

    Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533

    Article  Google Scholar 

  34. 34.

    Sivanandam SN, Deepa SN (2006) Introduction to neural networks using Matlab 6.0. Tata McGraw-Hill Education, New Delhi

    Google Scholar 

  35. 35.

    Jang JSR, Sun CT, Mizutani E (1997) A computational approach to learning and machine intelligence. Prentice Hall Inc, Upper Saddle River

    Google Scholar 

  36. 36.

    Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, New York

    Google Scholar 

  37. 37.

    Verma T, Dubey S, Sabrol H (2017) Color image segmentation of disease infected plant images captured in an uncontrolled environment. In: International conference on next generation computing technologies. Springer, Singapore, pp 790–804

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Toran Verma.

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

Verify currency and authenticity via CrossMark

Cite this article

Verma, T., Dubey, S. Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images. Arch Computat Methods Eng 27, 1611–1632 (2020). https://doi.org/10.1007/s11831-019-09364-6

Download citation