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New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques

Abstract

One of the major concerns for fruit selling companies, at present, is to find an effective way for rapid classification and detection of fruit defects. Olive is one of the most important agricultural product, which receives great attention from fruit and vegetables selling companies, for its utilization in various industries such as oils and pickles industry. The small size and multiple colours of the olive fruit increases the difficulty of detecting the external defects. This paper presents new efficient methods for detecting and classifying automatically the external defects of olive fruits. The proposed techniques can separate between the defected and the healthy olive fruits, and then detect and classify the actual defected area. The proposed techniques are based on texture analysis and the homogeneity texture measure. The results and the performance of proposed techniques were compared with varies techniques such as Canny, Otsu, local binary pattern algorithm, K-means, and Fuzzy C-Means algorithms. The results reveal that proposed techniques have the highest accuracy rate among other techniques. The simplicity and the efficiency of the proposed techniques make them appropriate for designing a low-cost hardware kit that can be used for real applications.

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References

  • Arivazhagan, S., Shebiah, R. N., Nidhyanandhan, S. S., & Ganesan, L. (2010). Fruit recognition using color and texture features. Journal of Emerging Trends in Computing and Information Sciences, 1(2), 90.

    Google Scholar 

  • Bianconi, F., Ceccarelli, L., Fernández, A., & Saetta, S. A. (2014). A sequential machine vision procedure for assessing paper impurities. Computers in Industry, 65(2), 325–332.

    Article  Google Scholar 

  • Chihaoui, M., Elkefi, A., Bellil, W., & Amar, C. (2016). A novel face recognition system based on skin detection, HMM and LBP. International Journal of Computer Science and Information Security (IJCSIS), 14(6), 308–316.

    Google Scholar 

  • Chudasama, D., & Patel, T. (2015). Image segmentation using morphological operations. International Journal of Computer Applications, 117(18), 0975–8887.

    Article  Google Scholar 

  • Déniz, O., Castrillón, M., & Hernández, M. (2003). Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters, 24(13), 2153–2157.

    Article  MATH  Google Scholar 

  • Furferi, R., Governi, L., & Volpe, Y. (2010). ANN-based method for olive ripening index automatic prediction. Journal of Food Engineering, 101(3), 318–328.

    Article  Google Scholar 

  • Gandhi, I., & Andiyammal, M. P. (2015). Infected Fruit Part Detection Using Clustering. International Journal of Current Research, 7(03), 13866–13871.

    Google Scholar 

  • Gatica, G., Best, S., Ceroni, J., & Lefranc, G. (2013). Olive fruits recognition using neural networks. Procedia Computer Science, 17, 412–419.

    Article  Google Scholar 

  • Guoying, Z., & Pietikainen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 915–928.

    Article  Google Scholar 

  • Huang, J., Kumar, S.R., Mitra, M., Zhu, W. J., & Zabih, R. (1997). Image indexing using color correlograms. In Proceedings of IEEE international conference on computer vision and pattern recognition, (pp. 762–768).

  • Jitendrasinh, G. R. (2015). A review on fuzzy C-mean clustering algorithm. International Journal of Modern Trends in Engineering and Research (IJMTER), 02(02), 751–754.

    Google Scholar 

  • Khade, S., Pandhare, P., Navale, S., Patil, K., & Gaikwad, V. (2016). Fruit quality evaluation using k-means clustering segmentation approach. International Journal of Advances in Science Engineering and Technology, 4(2), 27–31.

    Google Scholar 

  • Khoje, S. A., Bodhe, S. K., & Adsul, A. (2013). Automated skin defect identification system for fruit grading based on discrete curvelet transform. International Journal of Engineering and Technology (IJET), 5(4), 3251.

    Google Scholar 

  • 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 

  • Kith, K., Van Wyk, B. J., & Van Wyk, M. L. A. (2008). The normalized wavelet descriptor for shape retrieval. International Journal of Wavelets, Multiresolution and Information Processing, 6(1), 25–36.

    MathSciNet  Article  MATH  Google Scholar 

  • Liu, L., Fieguth, P., Zhao, G., Pietikäinen, M., & Hu, D. (2016a). Extended local binary patterns for face recognition. Information Sciences, 358–359, 56–72.

    Article  Google Scholar 

  • Liu, L., Lao, S., Fieguth, P. W., et al. (2016b). Median robust extended local binary pattern for texture classification. IEEE Transactions on Image Processing, 25(3), 1368–1381.

    MathSciNet  Article  MATH  Google Scholar 

  • Ma, W. Y., & Zhang, H. J. (2013). Image indexing and retrieval-in handbook of grading based on discrete curvelet transform. International Journal of Engineering and Technology (IJET), 5(4), 4763–4769.

    Google Scholar 

  • Malakar, A., & Mukherjee, J. (2013). Image clustering using color moments, histogram, edge and K-means clustering. International Journal of Science and Research (IJSR), 2(1), 2319.

    Google Scholar 

  • Manual_CV-A20CL_CV-A80CL_Aug08 Digital monochrome / color HDTV (1080p) CMOS camera, 2008 JAI.

  • Mia, S., & Rahman, M. M. (2018). An efficient image segmentation method based on linear discriminant analysis and K-means algorithm with automatically splitting and merging clusters. International Journal of Imaging and Robotics, 18(1), 62–72.

    Google Scholar 

  • Nashat, A., & Hassan, N. (2017). Automatic segmentation and classification of olive fruits batches based on discrete wavelet transform and visual perceptual texture features. International Journal of Wavelets, Multiresolution and Information Processing, 16(1), 1850003.

    MathSciNet  Article  MATH  Google Scholar 

  • Nayagam, R. D. (2016). Implementation of external defects detection system to classify the fruits. International Journal of Innovative Research in Computer and Communication Engineering, 4(2), 1850003.

    Google Scholar 

  • Puerto, D. A., Gila, D. M. M., García, J. G., & Ortega, J. G. (2015). Sorting olive batches for the milling process using image processing. Sensors, 15, 15738–15754.

    Article  Google Scholar 

  • Pujitha, N., Swathi, C., & Kanchana, V. (2016). Detection of external defects on mango. International Journal of Applied Engineering Research, 11(7), 4763–4769.

    Google Scholar 

  • Safad, T., Kang, M., Leite, I. C. C., & Vidakovic, B. (2016). Wavelet-based spectral descriptors for detection of damage in sunflower seeds. International Journal of Wavelets, Multiresolution and Information Processing, 14(4), 1650027.

    MathSciNet  Article  MATH  Google Scholar 

  • Satone, M., Diwakar, S., & Joshi, V. (2017). Automatic bruise detection in fruits using thermal images. International Journal of Advanced Research in Computer Science and Software Engineering, 7(5), 727–732.

    Article  Google Scholar 

  • Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13, 146–165.

    Article  Google Scholar 

  • Sugiyama, M. (2006). Local fisher discriminant analysis for supervised dimensionality reduction. In Proceedings of the 23rd international conference on machine learning, Pittsburgh, PA, June 25–29 (pp. 905–912).

  • Suresha, M., & Danti, A. (2012). Construction of co-occurrence matrix using gabor wavelets for classification of arecanuts by decision trees. International Journal of Applied Information Systems (IJAIS), 4(6), 33.

    Article  Google Scholar 

  • Vijayarajan, R., & Muttan, S. (2016). Spatial weighted fuzzy c-means clustering based principal component averaging image fusion. International Journal of Tomography & Simulation, 29(3), 104–113.

    Google Scholar 

  • Wang, J. (2013). A visual word-based leaf classification scheme. International Journal of Applied Mathematics and Statistics, 51(22), 233–240.

    Google Scholar 

  • Zeng, Q. M., Zhu, T. L., Zhuang, X. Y., & Zheng, M. X. (2015). Periodic wavelet descriptor of plant leaf and its application in botany. International Journal of Wavelets, Multiresolution and Information Processing, 13(6), 1550043.

    MathSciNet  Article  MATH  Google Scholar 

  • Zhang, L., Yan, L., & Pingling, D. (2017a). Odor recognition in multiple e-nose systems with cross-domain discriminative subspace learning. IEEE Transactions on Instrumentation and Measurement, 66(7), 1679–1692.

    Article  Google Scholar 

  • Zhang, L., Yang, J., & Zhang, D. (2017b). Domain class consistency based transfer learning for image classification across domains. Information Sciences, 418–419, 242–257.

    Article  Google Scholar 

  • Zhang, L., & Zhang, D. (2016). Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Transactions on Image Processing, 25(10), 4959–4973.

    MathSciNet  Article  MATH  Google Scholar 

  • Zhang, L., & Zhang, D. (2017). Evolutionary cost-sensitive extreme learning machine. IEEE Transactions on Neural Networks and Learning Systems, 28(12), 3045–3060.

    MathSciNet  Article  Google Scholar 

  • Zhang, L., Zuo, W., & Zhang, D. (2016). Latent sparse domain transfer learning for visual adaptation. IEEE Transactions on Image Processing, 25(3), 1177–1191.

    MathSciNet  Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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Correspondence to Ahmed A. Nashat.

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Hussain Hassan, N.M., Nashat, A.A. New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques. Multidim Syst Sign Process 30, 571–589 (2019). https://doi.org/10.1007/s11045-018-0573-5

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  • DOI: https://doi.org/10.1007/s11045-018-0573-5

Keywords

  • Image segmentation techniques
  • Features extraction
  • Image convolution techniques
  • Artificial vision techniques
  • Olive fruit classification techniques

Mathematics Subject Classification

  • 62H35
  • 62H30
  • 62H15
  • 62H20