Skip to main content
SpringerLink
Log in

Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images

  • Published:
Precision Agriculture Aims and scope Submit manuscript

A Correction to this article was published on 17 May 2018

This article has been updated

Abstract

Early detection and counting of immature green citrus fruit using computer vision can help growers produce a predictive yield map which could be used to adjust management practices during the fruit maturing stages. However, such detecting and counting is difficult because of varying illumination, random occlusion and color similarity with leaves. An immature fruit detection algorithm was developed with the aim of identifying and counting fruit in a citrus grove under varying illumination environments and random occlusions using images acquired by a regular red–green–blue (RGB) color camera. Acquired citrus images included front-lighting and back-lighting illumination conditions. The Retinex image enhancement algorithm and the two-dimensional discrete wavelet transform were used for image illumination normalization. Color-based K-means clustering and circular hough transform (CHT) were applied in order to detect potential fruit regions. A Local Binary Patterns feature-based Adaptive Boosting (AdaBoost) classifier was built for removing false positives. A sub-window was used to scan the difference image between the illumination-normalized image and the resulting image from CHT detection in order to detect small areas and partially occluded fruit. An overall accuracy of 85.6% was achieved for the validation set which showed promising potential for the proposed method.

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

Access this article

Log in via an institution

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

  • 17 May 2018

    The original version of this article unfortunately contained a mistake. The affiliation “China West Normal University” should be removed from the author Dr. Chenglin Wang. The correct affiliation details are given below.

Abbreviations

LBP:

Local Binary Patterns

RGB:

Red, green and blue

CHT:

Circle hough transform

FFT:

Fast Fourier transform

ARB:

Adaptive red and blue chromatic map

FNCC:

Fast normalized cross correlation

Retinex:

A compound word from ‘retina’ and ‘cortex’

SVM:

Support vector machines

LS- SVM:

The least squares support vector machine

AdaBoost:

Abbreviations of adaptive boosting

References

  • Bansal, R., Lee, W. S., & Satish, S. (2013). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture, 14(1), 59–70.

    Article  Google Scholar 

  • Cheng, Y., Hou, Y., Zhao, C., Li, Z., Hu, Y., & Wang, C. (2010). Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain. Neurocomputing, 73, 2217–2224.

    Article  Google Scholar 

  • Du, S., & Ward, R. (2005). Wavelet-based illumination normalization for face recognition. In 2005 International Conference on Image Processing (Vol. 3, pp. 954–957). IEEE Computer Society, Genova, Italy.

  • Duan, J., Zhou, C. G., Liu, X. H., Zhang, L. B., & Liu, M. (2004). The methods of improving variable illumination for face recognition. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics (pp. 3918–3923). IEEE Computer Society, Shanghai City, China.

  • Duda, R. O., & Hart, P. E. (1972). Use of the Hough transform to detect lies and curves in pictures. Communications of the ACM, 15(1), 11–15.

    Article  Google Scholar 

  • Durand, F., & Dorsey, J. (2002). Fast bilateral filtering for the display of high dynamic range images. SIGGRAPH, 2002, 257–266.

    Google Scholar 

  • Edwin, H. Land. (1977). The retinex theory of color vision. Scientific American, 237(6), 108–129.

    Article  Google Scholar 

  • Emadi, M., Khalid, M., Yusof, R., & Navabifar, F. (2012). Illumination Normalization using 2D Wavelet. Procedia Engineering, 41, 854–859.

    Article  Google Scholar 

  • Freund, Y., & Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi P. (Ed) Computational Learning Theory. EuroCOLT 1995. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence) (Vol. 904). Berlin, Heidelberg, Germany: Springer.

    Google Scholar 

  • Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.

    Article  Google Scholar 

  • Kane, K. E., & Lee, W. S. (2007). Multispectral imaging for in-field green citrus identification. ASABE Paper No. 073025. St. Joseph, MI, USA: ASABE.

  • Khammari, A., Nashashibi, F., Abramson, Y., & Laurgeau, C. (2005). Vehicle detection combining gradient analysis and AdaBoost classification. In Proceedings of 2005 Intelligent Transportation Systems (pp. 66–71). IEEE Computer Society, Vienna, Austria.

  • Kurtulmus, F., Lee, W. S., & Vardar, A. (2011). Green citrus detection using “eigenfruit”, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture, 78(2), 140–149.

    Article  Google Scholar 

  • Kurtulmus, F., Lee, W. S., & Vardar, A. (2014). Immature peach detection in color images acquired in natural illumination conditions using statistical classifiers and neural network. Precision Agriculture, 15, 57–79.

    Article  Google Scholar 

  • Lei, J., Han, H., & Tong, M. (2012). Efficient visual tracking by using LBP descriptor. International Conference on Artificial Intelligence and Computer Engineering, 7530, 391–399.

    Article  Google Scholar 

  • Li, H., Lee, W. S., & Wang, K. (2016). Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor color images. Precision Agriculture, 17, 678–697.

    Article  Google Scholar 

  • Linker, R., Cohen, O., & Naor, A. (2012). Determination of the number of green apples in RGB images recorded in orchards. Computers and Electronics in Agriculture, 81(2), 45–57.

    Article  Google Scholar 

  • Luo, L. F., Tang, Y. C., Zou, X. J., Wang, C. L., Zhang, P., & Feng, W. X. (2016). Robust grape cluster detection in a vineyard by combining the AdaBoost framework and multiple color components. Sensors, 16(12), 1–20.

    Article  Google Scholar 

  • Mazo, C., Alegre, E., & Trujillo, M. (2017). Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM. Computer Methods and Programs in Biomedicine, 147, 1–10.

    Article  Google Scholar 

  • Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29, 51–59.

    Article  Google Scholar 

  • Okamoto, H., & Lee, W. S. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66, 201–208.

    Article  Google Scholar 

  • Savvides, M., & Kumar, V. (2003). Illumination normalization using logarithm transforms for face authentication. In Proceedings of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication (pp. 549–556). Guildford, UK: Springer.

    Google Scholar 

  • Sengupta, S., & Lee, W. S. (2014). Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosystems Engineering, 117, 51–61.

    Article  Google Scholar 

  • Stajnko, D., Lakota, M., & Hoevar, M. (2004). Estimation of number and diameter of apple fruit in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture, 42, 31–42.

    Article  Google Scholar 

  • Sun, J. G., Li, Y., Yang, X. N., & Wang, J. T. (2011). Face recognition based on improved LBP and LS-SVM. Advanced Materials Research, 403–408, 3249–3252.

    Article  Google Scholar 

  • United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS). (2015). Forecasting Florida citrus production (brochure). Retrieved June 28, 2017 from http://usda.mannlib.cornell.edu/usda/nass/CitrFrui//2010s/2015/CitrFrui-09-17-2015.pdf.

  • Vishwakarma, V. P., Pandey, S., & Gupta, M. N. (2009). Adaptive histogram equalization and logarithm transform with rescaled low-frequency DCT coefficients for illumination normalization. International Journal of Recent Trends in Engineering, Issue on Computer Science, 1, 318–322.

    Google Scholar 

  • Wachs, J. P., Stern, H. I., Burks, T., & Alchanatis, V. (2010). Low and high-level visual feature-based apple detection from multi-modal images. Precision Agriculture, 11, 717–735.

    Article  Google Scholar 

  • Wang, Y. B., Ai, H. Z., Wu, B., & Huang, C. (2004). Real time facial expression recognition with AdaBoost. In Proceedings of the 17th International Conference on Pattern Recognition (pp. 926–929). IEEE Computer Society, Cambridge, UK.

  • Yang, Y. N., Jiang, Z. H., Yang, C. H., Xia, Z. Q., & Liu, F. (2015). Improved retinex image enhancement algorithm based on bilateral filtering. In Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (Vol. 39, pp. 2250–2256). Xi’an, China: Atlantis Press.

  • Zhao, C. Y., Lee, W. S., & He, D. (2016). Immature green citrus detection based on color feature and sum of absolute transformed difference (SATD) using color images in the citrus grove. Computers and Electronics in Agriculture, 124, 243–253.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the National Natural Science Foundation of China and Science, technology project of Guangdong Province, technology project of Huizhou (Nos. 31571568, 2015A020209111, 2014B040008006), and support from the University of Florida.

Author information

Authors and Affiliations

  1. School of Electronic Information Engineering, China West Normal University, Nanchong, 637002, China

    Chenglin Wang

  2. Department of Agricultural & Biological Engineering, University of Florida, Gainesville, FL, 32611, USA

    Won Suk Lee, Hao Gan & Justice Diamond

  3. College of Engineering, South China Agricultural University, Guangzhou, 510642, China

    Chenglin Wang & Xiangjun Zou

  4. Department of Agricultural & Biological Engineering, Penn State University, University Park, PA, 16802, USA

    Daeun Choi

Authors
  1. Chenglin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Won Suk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangjun Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daeun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hao Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Justice Diamond
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Won Suk Lee or Xiangjun Zou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Lee, W.S., Zou, X. et al. Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images. Precision Agric 19, 1062–1083 (2018). https://doi.org/10.1007/s11119-018-9574-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-018-9574-5

Keywords

Search

Navigation