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Precision Agriculture

, Volume 15, Issue 1, pp 57–79 | Cite as

Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network

  • Ferhat KurtulmusEmail author
  • Won Suk Lee
  • Ali Vardar
Article

Abstract

Detection of immature peach fruits would help growers to create yield maps which are very useful tools for adjusting management practices during the fruit maturing stages. Machine vision algorithms were developed to detect and count immature peach fruit in natural canopies using colour images. This study was the first effort to detect immature peach fruit in natural environment to the authors’ knowledge. Captured images had various illumination conditions due to both direct sunlight and diffusive light conditions that make the fruit detection task more difficult. A training set and a validation set were used to develop and to test the algorithms. Different image scanning methods including finding potential fruit regions were developed and used to parse fruit objects in the natural canopy image. Circular Gabor texture analysis and ‘eigenfruit’ approach (inspired by the ‘eigenface’ face detection and recognition method) were used for feature extraction. Statistical classifiers, a neural network and a support vector machine classifier were built and used for detecting peach fruit. A blob analysis was performed to merge multiple detections for the same peach fruit. Performance of the classifiers and image scanning methods were introduced and evaluated. Using the proposed algorithms, 84.6, 77.9 and 71.2 % of the actual fruits were successfully detected using three different image scanning methods for the validation set.

Keywords

Computer vision Fruit detection Immature peach Yield mapping Statistical classifiers 

References

  1. Bulanon, D. M., Burks, T. F., & Alchanatis, V. (2010). A multispectral imaging analysis for enhancing citrus fruit detection. Environment Control in Biology, 48(2), 82–91.CrossRefGoogle Scholar
  2. Deconinck, E., Sacréa, P. Y., Coomans, D., & De Beer, J. (2012). Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines. Journal of Pharmaceutical and Biomedical Analysis, 57, 68–75.PubMedCrossRefGoogle Scholar
  3. FAO 2010. Food and agriculture organization of the United Nations http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor. Accessed 12 Dec 2012.
  4. Guo, Y., Hastie, T., & Tibshirani, R. (2005). Regularized discriminant analysis and its application in microarrays. Biostatistics, 1(1), 1–18.Google Scholar
  5. Gupta, V., Singh, G., Mittal, M., & Pahuja, S. K. (2010). Fourier transform of untransformable signals using pattern recognition technique. In Proceedings of the second international conference on advances in computing, control, and telecommunication technologies (ACT’10). IEEE Computer Society, Washington, DC, USA, pp. 6–9.Google Scholar
  6. Jimenez, A. R., Ceres, R., & Pons, J. L. (2000). A survey of computer vision methods for locating fruit on trees. Transactions of the ASAE, 43, 1911–1920.CrossRefGoogle Scholar
  7. Kecman, V. (2001). Learning and soft computing. Cambridge: MIT Press.Google Scholar
  8. Keuchel, J., Naumann, S., Heiler, M., & Siegmund, A. (2003). Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sensing of Environment, 86, 530–541.CrossRefGoogle Scholar
  9. Kurtulmuş, 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.CrossRefGoogle Scholar
  10. Loy, G., & Zelinsky, A. (2003). Fast radial symmetry for detecting points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 8.CrossRefGoogle Scholar
  11. Okamoto, H., & Lee, W. S. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66, 201–208.CrossRefGoogle Scholar
  12. Parrish, E. A, Jr, & Goksel, A. K. (1977). Pictorial pattern recognition applied to harvesting. Transactions of the ASAE, 20, 822–827.CrossRefGoogle Scholar
  13. Pla, F., Juste, F., & Ferri, F. (1993). Feature extraction of spherical objects in image analysis: an application to robotic citrus harvesting. Computers and Electronics in Agriculture, 8, 57–72.CrossRefGoogle Scholar
  14. Questier, F., Put, R., Coomans, D., Walczak, B., & Heyden, Y. V. (2005). The use of CART and multivariate regression trees for supervised and unsupervised feature selection. Chemometrics and Intelligent Laboratory Systems, 76, 45–54.CrossRefGoogle Scholar
  15. Stajnko, D., Lakota, M., & Hoevar, M. (2004). Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture, 42, 31–42.CrossRefGoogle Scholar
  16. Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3, 71–86.PubMedCrossRefGoogle Scholar
  17. Wachs, J., Stern, H. I., Burks, T., & Alchanatis, V. (2009). Apple detection in natural tree canopies from multimodal images. In Proceedings of the joint international agricultural conference, JIAC, Wageningen Academic |Publishers, The Netherlands, pp. 293–302.Google Scholar
  18. Xiang, D., Tian, J., Deng, K., Zhang, X., Yang, F., & Wan, X. (2011). Retinal vessel extraction by combining radial symmetry transform and iterated graph cuts. In Proceedings of IEEE Engineering in Medicine and Biology Society 2011, Boston, Massachusetts, USA, pp. 3950–3953.Google Scholar
  19. Zhang, J., Tan, T., & Ma, L. (2002). Invariant texture segmentation via circular Gabor filters. In Proceedings of the 16th international conference on pattern recognition. IEEE Computer Society, Quebec City, Quebec, Canada, 2002, (2), pp. 901–904.Google Scholar
  20. Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. Graphics Gems IV, 474–485.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Biosystems Engineering, Faculty of AgricultureUludag UniversityBursaTurkey
  2. 2.Department of Agricultural and Biological EngineeringUniversity of FloridaGainesvilleUSA

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