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Performance analysis of soft computing techniques for the automatic classification of fruits dataset

  • L. RajasekarEmail author
  • D. Sharmila
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  • 27 Downloads

Abstract

Different properties of numerous types of fruits and vegetable classification are still an intricate task. The soft computing strategies are used to recognize a fruit by blending the three basic features which characterize the object: color, shape and texture. The classifiers are relatively effective, when the image feature vector is fused with one another. This technique decreases the dimensionality of the feature vector. So the combined and normalized features of the image are producing better classification accuracy with minimum number of training data. K-nearest neighbor (K-NN), linear discriminant analysis, naive Bayes, error-correcting output classifier and decision tree classifiers are used for image recognition process. A tenfold cross-validation technique is used to improve the classification accuracy of the classifier. The experiment is demonstrated in all the five techniques with 2400 images from the 24 categories of fruits and vegetables. The K-NN scored 97.5% of classification accuracy.

Keywords

K-nearest neighbor Naives Bayes Decision tree Classification Fruits dataset 

Notes

Compliance with ethical standard

Conflict of interest

The authors declare that they have no conflicts of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants performed by any of the authors.

References

  1. Allwein EL, Schapire RE, Singer Y (2001) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113–141MathSciNetzbMATHGoogle Scholar
  2. Anandakumar H, Umamaheswari K (2017) Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Comput.  https://doi.org/10.1007/s10586-017-0798-3 Google Scholar
  3. Anandakumar H, Umamaheswari K (2018) A bio-inspired swarm intelligence technique for social aware cognitive radio handovers. Comput Electr Eng 71:925–937Google Scholar
  4. Armand S, Watelain E, Roux E, Mercier M, Lepoutre FX (2007) Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees. Gait Posture 25:475–484Google Scholar
  5. Arulmurugan R, Sabarmathi KR, Anandakumar H (2017) Classification of sentence level sentiment analysis using cloud machine learning techniques. Cluster Comput 28:1–11.  https://doi.org/10.1007/s10586-017-1200-1 Google Scholar
  6. Ashutosh S, Sheikh SH, Khan T, Kumar A (2017) Recognition and detection of fruits diseases using machine learning techniques. Int J Adv Eng Res Dev 4(3):303–305Google Scholar
  7. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs Fisherfaces: recognition using Class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720Google Scholar
  8. Chen PC, Pavlidis T (1979) Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm. Comput Vis Graph 10(2):172–182Google Scholar
  9. Cornejo JYR, Pedrini H (2016) Automatic fruit and vegetable recognition based on CENTRIST and color representation. In: Progress in pattern recognition, image analysis, computer vision, and applications, 21st Iberoamerican Congress, CIARP 2016, Lima, PeruGoogle Scholar
  10. De Jesús Rubio J (2015) A method with neural networks for the classification of fruits and vegetables. Soft Comput 21(23):1–14Google Scholar
  11. Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error- correcting output codes. J Artif Intell Res 2:263–286zbMATHGoogle Scholar
  12. Duan K, Keerthi SS, Chu W, Shevade SK, Poo AN (2003) Multi-category classification by soft-max combination of binary classifiers, vol 2709. Lecture Notes in Computer Science. Springer, Berlin/HeidelbergzbMATHGoogle Scholar
  13. Dubey SR, Jalal AS (2015) Apple disease classification using color, texture and shape features from images. Signal Image Process Video Process 10:819–826Google Scholar
  14. Etemad K, Chellappa R (1997) Discriminant analysis for face recognition of human face images. J Opt Soc Am A/Vol 14:1724–1733Google Scholar
  15. Gao G, Zhao S, Zhang C, Yu X, Li Z (2015) Study on fruit recognition methods based on compressed sensing. J Comput Theor Nanosci 12(9):2937–2942Google Scholar
  16. Gonzalez R, Woods R (2007) Digital image processing, 3rd edn. Prentice-Hall, CambridgeGoogle Scholar
  17. Hong J, Min J, Cho U, Cho S (2008) Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers. Pattern Recogn 41(2):662–671zbMATHGoogle Scholar
  18. Kubanek M, Smorawa D, Holotyak T (2015) Feature extraction of palm vein patterns based on two-dimensional density function. In: Artificial intelligence and soft computing—14th international conference, ICAISC 2015, Zakopane, Poland, June 14–28, 2015, Proceedings, pp 101–111Google Scholar
  19. Lu J, Plataniotis KN, Venetsanopoulos AA (2003) Face recognition using LDA- based algorithms. IEEE Trans Neural Netw 14(1):1–4Google Scholar
  20. Mahesh S, Jayas DS, Paliwal J, White NDG (2015) Hyperspectral imaging to classify and monitor quality of agricultural materials. J Stored Prod Res 61:17–26Google Scholar
  21. May RJ, Maier HR, Dandy GC (2010) Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw 23:283–294Google Scholar
  22. McCool C, Sa I, Dayoub F, Lehnert C, Perez T, Upcroft B (2016) Visual detection of occluded crop: for automated harvesting. In: Proceedings of the international conference on robotics and automation, Stockholm, SwedenGoogle Scholar
  23. Nowak B, Nowicki R, Woźniak M, Napoli C (2015) Multi-class nearest neighbour classifier for incomplete data handling. In: Artificial intelligence and soft computing, ser. Lecture notes in computer science. Springer International Publishing, 2015, vol. 9119, pp 469–480Google Scholar
  24. Ren S, He K, Girshick R, Faster SJ (2015) R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the advances in neural information processing systems, Montréal, pp 91–99Google Scholar
  25. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211–252.  https://doi.org/10.1007/s11263-015-0816 MathSciNetGoogle Scholar
  26. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, pp 1–9Google Scholar
  27. Wang J, Neskovic P, Cooper LN (2007) Improving nearest neighbour rule with a simple adaptive distance measure. Pattern Recogn Lett 28(2):207–213Google Scholar
  28. Wang Y, Wang Y, Shi C, Shi H (2016) Research on feature fusion technology of fruit and vegetable image recognition based on SVM. In: Social computing, second international conference of young computer scientists, engineers and educators, ICYCSEE 2016, Harbin, ChinaGoogle Scholar
  29. Zhang Yudong, Phillips Preetha, Wang Shuihua, Ji Genlin, Yang Jiquan, Jianguo Wu (2016) Fruit classification by biogeography-based optimization and feedforward neural network. Exp Syst 33(3):239–253Google Scholar
  30. Zhou Y, Li Y, Xia S (2009) An improved KNN text classification algorithm based on clustering. J Comput 4(3):230–237Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringBannari Amman Institute of TechnologySathyamangalamIndia
  2. 2.Department of Information TechnologyDr. N.G.P. Institute of TechnologyCoimbatoreIndia

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