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Detection of Fruits Image Applying Decision Tree Classifier Techniques

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Computational Intelligence and Data Analytics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 142))

Abstract

Recognition of the image of fruits and vegetables is proposed through various descriptors based on decision tree classifier techniques. An accurate and efficient recognition system for fruits and vegetables is one major challenge. To solve these challenges, we have examined various feature descriptors based on colour, texture and shape (its combination also). In this paper, Otsu’s thresholding is used for background subtraction. Further, all segmented image is used in the feature extraction phase. Furthermore, C4.5 are used for training and classification. Finally, the various performances metric such as Accuracy, Sensitivity, Specificity, Precision, False positive rate, False-negative rate is utilized to evaluate the proposed system for recognition problem. We, also analysis the performance accuracy of both classifiers. The outcome demonstrates that the proposed fused descriptor based on the state of colour, texture, shape is more efficient.

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References

  1. Saxana SP, Mamata (2017) At a glance 2017. Horticultural statistics at a glance

    Google Scholar 

  2. “Exports From India of Fruits & Vegetables. [Online]. Available: http://agriexchange.apeda.gov.in/product_profile/exp_f_india.aspx?categorycode=0102. (Accessed 06 Sep 2019).

  3. Pass G, Zabih R, Miller J (1996) Comparing images using color coherence vectors. In: 1996 Proceedings of the fourth ACM international conference on Multimedia—MULTIMEDIA ’96, pp 65–73

    Google Scholar 

  4. Stehling RO, Nascimento MA, Falcao AX (2002) A compact and efficient image retrieval approach based on border/interior pixel classification. In: International conference on information and knowledge management (CIKM), pp 102–109

    Google Scholar 

  5. Zhang Y, Wu L (2012) Classification of fruits using computer vision and a multiclass support vector machine. Sensors (Switzerland) 12(9):12489–12505

    Article  Google Scholar 

  6. Rocha A, Hauagge DC, Wainer J, Goldenstein S (2010) Automatic fruit and vegetable classification from images. Comput Electron Agric 70(1):96–104

    Article  Google Scholar 

  7. Dubey SR, Jalal AS (2013) Species and variety detection of fruits and vegetables from images. Int J Appl Pattern Recogn 1(1):108

    Article  Google Scholar 

  8. Dubey SR, Jalal AS (2015) Fruit and vegetable recognition by fusing colour and texture features of the image using machine learning. Int J Appl Pattern Recogn 2(2):160

    Article  Google Scholar 

  9. Agarwal S, Awan A, Roth D (2004) Learning to detect objects in images via a sparse, part-based representation. IEEE Trans Pattern Anal Mach Intell 26(11):1475–1490

    Article  Google Scholar 

  10. Jurie F, Triggs B (2005) Creating efficient codebooks for visual recognition. Proc IEEE Int Conf Comput Vision I:604–610

    Article  Google Scholar 

  11. Marszalek M et al (2010) Spatial weighting for bag-of-features to cite this version : HAL Id : inria-00548584 spatial weighting for bag-of-features. In: 2010 Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 2118–2125

    Google Scholar 

  12. Tikkanen H, Alajoutsijärvi K, Tähtinen J A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10): 1615–1630, 200AD

    Google Scholar 

  13. Arivazhagan S, Shebiah RN, Nidhyanandhan SS, Ganesan L (2010) Fruit recognition using color and texture features. J Emerg Trends Comput Inf Sci 1(2):90–94

    Google Scholar 

  14. Berg AC, Berg TL, Malik J (2005) Shape matching and object recognition using low distortion correspondences. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), 1910 1:26–33

    Google Scholar 

  15. Moallem P, Serajoddin A, Pourghassem H (2017) Computer vision-based apple grading for golden delicious apples based on surface features. Inf Process Agric 4(1):33–40

    Google Scholar 

  16. Ohali YA (2011) Computer vision based date fruit grading system : design and implementation. J King Saud Univ—Comput Inf Sci, 23(1):29–36

    Google Scholar 

  17. Saleem G, Akhtar M, Ahmed N, Qureshi WS (2019) Automated analysis of visual leaf shape features for plant classification. Comput Electron Agric 157:270–280

    Article  Google Scholar 

  18. Tripathi MK, Maktedar DD (2019) A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: a survey. Inf Process Agric

    Google Scholar 

  19. Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90

    Article  Google Scholar 

  20. Hakak S et al (2019) Cloud-assisted gamification for education and learning—recent advances and challenges. Comput Electr Eng 74:22–34

    Article  Google Scholar 

  21. Benkercha R, Moulahoum S (2018) Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system. Sol Energy 173(April):610–634

    Article  Google Scholar 

  22. Kumar Y, Aggarwal A, Tiwari S, Singh K (2018) An efficient and robust approach for biomedical image retrieval using Zernike moments. Biomed Signal Process Control 39:459–473

    Article  Google Scholar 

  23. Emerson S, Kennedy R, O'Shea L, O'Brien J (2019) Trends and applications of machine learning in quantitative finance. In: 2019 8th international conference on economics and finance research (ICEFR)

    Google Scholar 

  24. Amrit C, Paauw T, Aly R, Lavric M (2017) Identifying child abuse through text mining and machine learning. Expert Syst Appl 88:402–418

    Article  Google Scholar 

  25. Chatterjee A, Gupta U, Chinnakotla MK, Srikanth R, Galley M, Agrawal P (2019) Understanding emotions in text using deep learning and big data. Comput Hum Behav 93:309–317

    Article  Google Scholar 

  26. Hossin M, Sulaiman N (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manage Proc 5(2):1–11

    Google Scholar 

  27. Boselli R, Cesarini M, Mercorio F, Mezzanzanica M (2018) Classifying online job advertisements through machine learning. Futur Gener Comput Syst 86:319–328

    Article  Google Scholar 

  28. Gautam R, Kaur P, Sharma M (2019) A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings. Prog Artif Intell

    Google Scholar 

  29. Kaur P, Sharma M (2019) Diagnosis of human psychological disorders using supervised learning and nature-inspired computing techniques: a meta-analysis. J Med Syst 43(7)

    Google Scholar 

  30. Naderi M, Khamehchi E, Karimi B (2019) Novel statistical forecasting models for crude oil price, gas price, and interest rate based on meta-heuristic bat algorithm. J Petrol Sci Eng 172:13–22

    Article  Google Scholar 

  31. Kumar M, Maktedar DD (2018) A framework with OTSU’S thresholding method for fruits and vegetables image segmentation. Int J Comput Appl 179(52):25–32

    Google Scholar 

  32. Ghosal S, Mehrotra R (Jun. 1997) A moment-based unified approach to image feature detection. IEEE Trans Image Process 6(6):781–793

    Article  Google Scholar 

  33. Pass G, Zabih R, Miller J (2004) Comparing images using color coherence vectors, pp 65–73

    Google Scholar 

  34. Güner A, Alçin ÖF, Şengür A (2019) Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features measurement vol 145, pp 214–225

    Google Scholar 

  35. Hassaballah M, Alshazly HA, Ali AA (Mar. 2019) Ear recognition using local binary patterns: a comparative experimental study. Expert Syst Appl 118:182–200

    Article  Google Scholar 

  36. Kou Q, Cheng D, Chen L, Zhuang Y (2019) Principal curvatures based local binary pattern for rotation invariant texture classification. Optik 193:162999

    Article  Google Scholar 

  37. Verma M, Raman B (2015) Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. J Vis Commun Image Represent 32:224–236

    Article  Google Scholar 

  38. Kanan HR, Salkhordeh S (2016) Rotation invariant multi-frame image super resolution reconstruction using Pseudo Zernike moments. Signal Process 118:103–114

    Article  Google Scholar 

  39. Singh C, Aggarwal A (2017) An efficient approach for image sequence denoising using Zernike moments-based nonlocal means approach. Comput Elect Eng 62:330–344

    Article  Google Scholar 

  40. Sharma M, Sharma S, Singh G (2018) Performance analysis of statistical and supervised learning techniques in stock data mining. Data 3(4):54

    Article  Google Scholar 

  41. Cheng JH, Chen HP, Lin YM (2010) A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5. Expert Syst Appl 37(3):1814–1820

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of E and TC, JJTU, Jhunjhunu, Rajasthan, India

    Shivendra

  2. JJTU, Jhunjhunu, Rajasthan, India

    Kasa Chiranjeevi

  3. Vasavi College of Engineering, Hyderabad, India

    Mukesh Kumar Tripathi

Authors
  1. Shivendra
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  2. Kasa Chiranjeevi
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  3. Mukesh Kumar Tripathi
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Corresponding author

Correspondence to Mukesh Kumar Tripathi .

Editor information

Editors and Affiliations

  1. Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  2. Computer Science School (FI), Technical University of Madrid, Madrid, Spain

    Susanna Munoz Hernandez

  3. Department of Information Technology, Vasavi College of Engineering, Hyderabad, India

    Ram Mohan Rao Kovvur

  4. Department of Information Technology, Vasavi College of Engineering, Hyderabad, India

    T. Hitendra Sarma

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shivendra, Chiranjeevi, K., Tripathi, M.K. (2023). Detection of Fruits Image Applying Decision Tree Classifier Techniques. In: Buyya, R., Hernandez, S.M., Kovvur, R.M.R., Sarma, T.H. (eds) Computational Intelligence and Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-19-3391-2_9

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