Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Artificial bee colony optimization (ABC) for grape leaves disease detection

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Plants are one of the important sources of food to the mankind. Plant diseases affect the economy of the country and also reduce the quality of agricultural product. Throughout the world, grapes are treated as a major fruit source, which contains nutrients like vitamin C. Diseases in the grape plants seriously affect the quality and production of grapes. Computer vision-based techniques have been used successfully in the last decades, to detect and classify the plants diseases. This paper deals with the identification and classification of diseases in grape leaves by using artificial bee colony (ABC) based feature selection. Initially the input images are applied with pre-processing steps which eliminate noises and background of the images. The features of color, texture and shape are extracted. ABC based attribute selection is used to find the optimal feature set. The selected features are given to the support vector machine classifier for foliar disease detection of grapes. The proposed method is experimented and compared with the other feature selection algorithms. The comparison results depict, the efficacy of the proposed method through the performance metrics such as recall, precision and classification accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abdul Kadir A (2014) Model of plant identification system using GLCM, lacunarity and Shen features. Res J Pharm Biol Chem Sci 5(2):1–10

    Google Scholar 

  • Andrushia AD, Thangarajan R (2015) Visual attention-based leukocyte image segmentation using extreme learning machine. Int J Adv Intell Paradig 7(2):172–186

    Article  Google Scholar 

  • Andrushia AD, Thangarajan R (2019) RTS-ELM: an approach for saliency-directed image segmentation with ripplet transform. Pattern Anal Appl. https://doi.org/10.1007/s10044-019-00800-8

    Article  MATH  Google Scholar 

  • Andrushia AD, Thangarjan R (2018) Saliency-based image compression using Walsh-Hadamard Transform (WHT) biologically rationalized computing techniques for image processing applications. Lect Notes Comput Vis Biomech 25:21–42

    Article  Google Scholar 

  • Andrushia AD, Trephena Patricia A (2019) Artificial bee colony based feature selection for automatic skin disease identification of mango fruit. In: Hemanth J, Balas V (eds) Nature inspired optimization techniques for image processing applications. Intelligent systems reference library, vol 150. Springer, pp 215–233

  • Angelov P, Kasabov N (2005) Evolving computational intelligence systems. In: First international workshop on genetic fuzzy systems, pp 76–82

  • Angelov P, Kasabov N (2006) Evolving intelligent systems. eIS IEEE SMC eNews Lett 15:1–13

    Google Scholar 

  • Arivazhagan S, Newlin Shebiah R, Ananthi S, Vishnu Varthini S (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15(1):211–217

    Google Scholar 

  • Bandi SR, Varadharajan A, Chinnasamy A (2013) Performance evaluation of various statistical classifiers in detecting the diseased citrus leaves. Int J Eng Sci Technol 5(2):298–307

    Google Scholar 

  • Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proceedings of the fifth annual workshop on computational learning theory. ACM, New York, pp 144–152

    Google Scholar 

  • Chen B, Chen L, Chen Y (2012) Efficient ant colony optimization for image feature selection. Signal Process 93:1566–1576

    Article  Google Scholar 

  • Gavhale KR, Gawande U, Hajari KO (2014) Unhealthy region of citrus leaf detection using image processing techniques. In: IEEE international conference on convergence of technology. https://doi.org/10.1109/i2ct.2014.7092035

  • Guth FA, Ward S, McDonnell K (2017) Autonomous disease detection in crops using deep learning. Biosyst Food Eng Res Rev 22:209

    Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 1973:610–621

    Article  Google Scholar 

  • Hassan SH, Yusup N, Awang Iskandar DNF (2017) Effective classification using artificial bee colony based feature selector. Telecommun Electron Comput Eng 9:3–11

    Google Scholar 

  • Hassanien AE, Gaber T, Mokhtarc 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 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, vol 200. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department

  • Kharde PK, Kulkarni HH (2016) An unique technique for grape leaf disease detection. Int J Sci Res Sci Eng Technol 2(4):343–348

    Google Scholar 

  • Kulkarni VR, Desai V (2016) ABC and PSO: a comparative analysis. In: IEEE international conference on computational intelligence and computing research

  • Kumar S, Sharma B, Sharma VK, Sharma H, Bansal JC (2018) Plant leaf disease identification using exponential spider monkey optimization. Sustain Comput Inf Syst. https://doi.org/10.1016/j.suscom.2018.10.004

    Article  Google Scholar 

  • Larabi Marie-Sainte S, Alalyani N (2018) Firefly algorithm based feature selection for Arabic text classification. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.06.004

    Article  Google Scholar 

  • Li Y, Zhou C, Zheng X (2014) The application of artificial bee colony algorithm in protein structure prediction. In: Pan L, Păun G, Pérez-Jiménez MJ, Song T (eds) Bio-inspired computing—theories and applications. Communications in computer and information science. Springer, Berlin, p 272

    Google Scholar 

  • Lin F, Liang D, Yeh C, Huang J-C (2014) Novel feature selection methods to financial distress prediction. Expert Syst Appl 41(5):2472–2483

    Article  Google Scholar 

  • Mokhtar U, Ali MAS, Hassenian AE, Hefny H (2015) Tomato leaves diseases detection approach based on support vector machines. In: 11th IEEE international computer engineering conference (ICENCO), pp 246–250

  • Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst Appl 41(4):2052–2064

    Article  Google Scholar 

  • Santana L, Silva L, Canuto A, Pintro F, Vale K (2010) A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers. In: IEEE congress on evolutionary computation, pp 1–8

  • Schiezaro M, Pedrini H (2013) Data feature selection based on Artificial Bee Colony algorithm EURASIP. J Image Video Process 1:47

    Article  Google Scholar 

  • Sharif M, Khana MA, Iqbala Z, Azama MF, Lalib IU, 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 

  • Sikora R, Piramuthu S (2007) Framework for efficient feature selection in genetic algorithm based data mining. Eur J Oper Res 180(2):723–737

    Article  Google Scholar 

  • Sun G, Jia X, Geng T (2018) Plant diseases recognition based on image processing technology. J Electr Comput Eng. https://doi.org/10.1155/2018/6070129

    Article  MathSciNet  Google Scholar 

  • Tan F, Fu XZ, Zhang YQ, Bourgeois A (2008) A genetic algorithm-based method for feature subset selection. Soft Comput 12(5):111–120

    Google Scholar 

  • Vijayalakshmi B, Mohan V (2016) Kernel-based PSO and FRVM: an automatic plant leaf type detection using texture, shape, and color features. Comput Electron Agric 125:99–112

    Article  Google Scholar 

  • Wäldchen J, Mäder P (2018) Plant species identification using computer vision techniques: a systematic literature review. Arch Comput Methods Eng 25:507–543

    Article  MathSciNet  Google Scholar 

  • Xu P, Wu G, Guo Y, Cheb X, Yang H, Zhang R (2017) Automatic wheat leaf rust detection and grading diagnosis via embedded image processing system. Procedia Comput Sci 107:836–841

    Article  Google Scholar 

  • Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43(6):1656–1671

    Article  Google Scholar 

  • Xue B, Zhang M, Will N, Browne XY (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626

    Article  Google Scholar 

  • Yadav S, Ekbal A, Saha S (2018) Feature selection for entity extraction from multiple biomedical corpora: a PSO-based approach. Soft Comput 22:6881–6904

    Article  Google Scholar 

  • Yan Z, Yuan C (2004) Ant colony optimization for feature selection in face recognition. Biometric Authentication Lect Notes Comput Sci 3072:221–226

    Article  Google Scholar 

  • Zhang S, Wang Z (2016) Cucumber disease recognition based on global-local singular value decomposition. Neurocomputing 205:341–348

    Article  Google Scholar 

  • Zhang S, Wu X, Youa Z, Zhang L (2017a) Leaf image-based cucumber disease recognition using sparse representation classification. Comput Electron Agric 134:135–141

    Article  Google Scholar 

  • Zhang X, Cui J, Wang W, Lin C (2017b) A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors 17:E1474. https://doi.org/10.3390/s17071474

    Article  Google Scholar 

  • Zivkovic Z, van der Heijden F (2006) Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognit Lett 27(7):773–780

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Karunya Universtiy, Coimbatore, India

    A. Diana Andrushia

  2. Department of ECE, Panimalar Engineering College, Chennai, India

    A. Trephena Patricia

Authors
  1. A. Diana Andrushia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Trephena Patricia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Diana Andrushia.

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

Andrushia, A.D., Patricia, A.T. Artificial bee colony optimization (ABC) for grape leaves disease detection. Evolving Systems 11, 105–117 (2020). https://doi.org/10.1007/s12530-019-09289-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-019-09289-2

Keywords

Search

Navigation